From addffbd9e6c0fc1a579f1daaba5842fa1c2226a2 Mon Sep 17 00:00:00 2001 From: igkonov Date: Fri, 6 Oct 2023 15:53:26 +0300 Subject: [PATCH 01/10] googlenet-v1-tf, googlenet-v2-tf, googlenet-v3 --- results/tflite_models_checklist.md | 26 +++++++++---------- .../validation/validation_results_tflite.md | 18 ++++++------- 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/results/tflite_models_checklist.md b/results/tflite_models_checklist.md index c98b8cd2a..c467b38e9 100644 --- a/results/tflite_models_checklist.md +++ b/results/tflite_models_checklist.md @@ -5,19 +5,19 @@ ### Image classification Model | Availability in OMZ (2023.03.10) | Availability in the validation table | --|-|-| -densenet-121|+-|+| -googlenet-v1-tf|+-|-| -googlenet-v2-tf|+-|-| -googlenet-v3|+-|-| -googlenet-v4-tf|+-|+| -mobilenet-v1-1.0-224-tf|+-|+| -mobilenet-v2-1.0-224|+-|+| -mobilenet-v2-1.4-224|+-|+| -mobilenet-v3-small-1.0-224|+-|+| -mobilenet-v3-large-1.0-224|+-|+| -inception-resnet-v2-tf|+|+| -resnet-50-tf|+-|+| +-|-|--------------------------------------| +densenet-121|+-| + | +googlenet-v1-tf|+-| + | +googlenet-v2-tf|+-| + | +googlenet-v3|+-| + | +googlenet-v4-tf|+-| + | +mobilenet-v1-1.0-224-tf|+-| + | +mobilenet-v2-1.0-224|+-| + | +mobilenet-v2-1.4-224|+-| + | +mobilenet-v3-small-1.0-224|+-| + | +mobilenet-v3-large-1.0-224|+-| + | +inception-resnet-v2-tf|+| + | +resnet-50-tf|+-| + | **Notes:** diff --git a/results/validation/validation_results_tflite.md b/results/validation/validation_results_tflite.md index ff5895720..558d582d2 100644 --- a/results/validation/validation_results_tflite.md +++ b/results/validation/validation_results_tflite.md @@ -17,9 +17,9 @@ Image resolution: 709 x 510 Model | Parameters | Python API | -|-|-| densenet-121|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.9523314 Granny Smith
0.0132282 orange
0.0125180 lemon
0.0027912 banana
0.0020333 piggy bank, penny bank| -googlenet-v1-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| -googlenet-v2-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| -googlenet-v3|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| +googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.6735930 strawberry
0.0737855 pill bottle
0.0155380 vault
0.0154004 plane, carpenter's plane, woodworking plane
0.0136552 sandal| +googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9849940 strawberry
0.0010004 fig
0.0009706 hay
0.0006835 thatch, thatched roof
0.0006694 jackfruit, jak, jack| +googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9867674 strawberry
0.0008529 binder, ring-binder
0.0005354 pill bottle
0.0003701 hay
0.0001682 pop bottle, soda bottle| googlenet-v4-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.9969806 Granny Smith
0.0001207 Rhodesian ridgeback
0.0000488 hair slide
0.0000473 pineapple, ananas
0.0000330 banana| mobilenet-v1-1.0-224-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.2949494 pitcher, ewer
0.1867124 saltshaker, salt shaker
0.1249271 necklace
0.0867643 piggy bank, penny bank
0.0360211 Granny Smith| mobilenet-v2-1.0-224|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.4164807 Granny Smith
0.3500757 piggy bank, penny bank
0.0358796 saltshaker, salt shaker
0.0147685 vase
0.0131548 pitcher, ewer| @@ -42,9 +42,9 @@ Image resolution: 500 x 500 Model | Parameters | Python API | -|-|-| densenet-121|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.9841611 junco, snowbird
0.0072198 chickadee
0.0034962 brambling, Fringilla montifringilla
0.0016226 water ouzel, dipper
0.0012858 indigo bunting, indigo finch, indigo bird, Passerina cyanea| -googlenet-v1-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| -googlenet-v2-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| -googlenet-v3|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| +googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.7443183 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0474523 goldfinch, Carduelis carduelis
0.0457429 water ouzel, dipper
0.0213391 house finch, linnet, Carpodacus mexicanus
0.0085102 junco, snowbird| +googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9265908 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0166747 goldfinch, Carduelis carduelis
0.0058714 water ouzel, dipper
0.0026126 kite
0.0022344 robin, American robin, Turdus migratorius| +googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9488292 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0005887 kite
0.0004797 jack-o'-lantern
0.0003071 robin, American robin, Turdus migratorius
0.0002692 cliff dwelling| googlenet-v4-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].| 0.9339716 junco, snowbird
0.0006892 chickadee
0.0005481 brambling, Fringilla montifringilla
0.0004948 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0004539 water ouzel, dipper| mobilenet-v1-1.0-224-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.7099941 junco, snowbird
0.2239839 chickadee
0.0195020 goldfinch, Carduelis carduelis
0.0140457 jay
0.0136091 brambling, Fringilla montifringilla| mobilenet-v2-1.0-224|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.3981952 junco, snowbird
0.0649636 chickadee
0.0456628 brambling, Fringilla montifringilla
0.0063850 water ouzel, dipper
0.0041957 goldfinch, Carduelis carduelis| @@ -67,9 +67,9 @@ Image resolution: 333 x 500 Model | Parameters | Python API | -|-|-| densenet-121|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].| 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| -googlenet-v1-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| -googlenet-v2-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| -googlenet-v3|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|-| +googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.1235979 breastplate, aegis, egis
0.1017586 lipstick, lip rouge
0.0949444 drum, membranophone, tympan
0.0817947 convertible
0.0486889 fire engine, fire truck| +googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.2662660 convertible
0.0966037 dogsled, dog sled, dog sleigh
0.0876837 breastplate, aegis, egis
0.0488674 beaker
0.0343599 drum, membranophone, tympan| +googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.4653829 beaker
0.3437532 breastplate, aegis, egis
0.0512180 suit, suit of clothes
0.0174647 lipstick, lip rouge
0.0134649 lighter, light, igniter, ignitor| googlenet-v4-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.6737013 lifeboat
0.0432948 submarine, pigboat, sub, U-boat
0.0322841 fireboat
0.0264144 beacon, lighthouse, beacon light, pharos
0.0147488 drilling platform, offshore rig| mobilenet-v1-1.0-224-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.1175058 lifeboat
0.1106691 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1055247 liner, ocean liner
0.0836357 beacon, lighthouse, beacon light, pharos
0.0784211 drilling platform, offshore rig| mobilenet-v2-1.0-224|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.2761748 beacon, lighthouse, beacon light, pharos
0.1192475 liner, ocean liner
0.0864237 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0541655 drilling platform, offshore rig
0.0266723 container ship, containership, container vessel| From bddb188d56fbef247446299ea3cc6628db01f14a Mon Sep 17 00:00:00 2001 From: igkonov Date: Fri, 6 Oct 2023 17:53:13 +0300 Subject: [PATCH 02/10] efficient-lite-fp32 efficient-lite-uint8 efficient-lite-int8 --- results/tflite_models_checklist.md | 48 ++++++------ .../validation/validation_results_tflite.md | 75 +++++++++++++++---- 2 files changed, 85 insertions(+), 38 deletions(-) diff --git a/results/tflite_models_checklist.md b/results/tflite_models_checklist.md index c467b38e9..e61d3e692 100644 --- a/results/tflite_models_checklist.md +++ b/results/tflite_models_checklist.md @@ -38,30 +38,30 @@ resnet-50-tf|+-| + | ### Image classification Model | Availability in TF hub (2023.03.10) | Availability in the validation table | --|-|-| -lite-model_mobilenet_v1_100_224_fp32_1.tflite|+ ([link][mobilenet_v1_100_224_fp32_1])|+| -lite-model_mobilenet_v2_100_224_fp32_1.tflite|+ ([link][mobilenet_v2_100_224_fp32_1])|+| -lite-model_mobilenet_v3_small_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_small_100_224_fp32_1])|+| -lite-model_mobilenet_v3_large_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_large_100_224_fp32_1])|+| -lite-model_mobilenet_v1_100_224_uint8_1.tflite|+ ([link][mobilenet_v1_100_224_uint8_1])|+| -lite-model_mobilenet_v2_100_224_uint8_1.tflite|+ ([link][mobilenet_v2_100_224_uint8_1])|+| -lite-model_mobilenet_v3_small_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_small_100_224_uint8_1])|+| -lite-model_mobilenet_v3_large_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_large_100_224_uint8_1])|+| -efficientnet_lite0_fp32_2.tflite|+ ([link][efficientnet_lite0_fp32_2])|-| -efficientnet_lite1_fp32_2.tflite|+ ([link][efficientnet_lite1_fp32_2])|-| -efficientnet_lite2_fp32_2.tflite|+ ([link][efficientnet_lite2_fp32_2])|-| -efficientnet_lite3_fp32_2.tflite|+ ([link][efficientnet_lite3_fp32_2])|-| -efficientnet_lite4_fp32_2.tflite|+ ([link][efficientnet_lite4_fp32_2])|-| -lite-efficientnet_lite0_uint8_2.tflite|+ ([link][efficientnet_lite0_uint8_2])|-| -lite-efficientnet_lite1_uint8_2.tflite|+ ([link][efficientnet_lite1_uint8_2])|-| -lite-efficientnet_lite2_uint8_2.tflite|+ ([link][efficientnet_lite2_uint8_2])|-| -lite-efficientnet_lite3_uint8_2.tflite|+ ([link][efficientnet_lite3_uint8_2])|-| -lite-efficientnet_lite4_uint8_2.tflite|+ ([link][efficientnet_lite4_uint8_2])|-| -efficientnet_lite0_int8_2.tflite|+ ([link][efficientnet_lite0_int8_2])|-| -efficientnet_lite1_int8_2.tflite|+ ([link][efficientnet_lite1_int8_2])|-| -efficientnet_lite2_int8_2.tflite|+ ([link][efficientnet_lite2_int8_2])|-| -efficientnet_lite3_int8_2.tflite|+ ([link][efficientnet_lite3_int8_2])|-| -efficientnet_lite4_int8_2.tflite|+ ([link][efficientnet_lite4_int8_2])|-| +-|-|--------------------------------------| +lite-model_mobilenet_v1_100_224_fp32_1.tflite|+ ([link][mobilenet_v1_100_224_fp32_1])| + | +lite-model_mobilenet_v2_100_224_fp32_1.tflite|+ ([link][mobilenet_v2_100_224_fp32_1])| + | +lite-model_mobilenet_v3_small_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_small_100_224_fp32_1])| + | +lite-model_mobilenet_v3_large_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_large_100_224_fp32_1])| + | +lite-model_mobilenet_v1_100_224_uint8_1.tflite|+ ([link][mobilenet_v1_100_224_uint8_1])| + | +lite-model_mobilenet_v2_100_224_uint8_1.tflite|+ ([link][mobilenet_v2_100_224_uint8_1])| + | +lite-model_mobilenet_v3_small_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_small_100_224_uint8_1])| + | +lite-model_mobilenet_v3_large_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_large_100_224_uint8_1])| + | +efficientnet_lite0_fp32_2.tflite|+ ([link][efficientnet_lite0_fp32_2])| + | +efficientnet_lite1_fp32_2.tflite|+ ([link][efficientnet_lite1_fp32_2])| + | +efficientnet_lite2_fp32_2.tflite|+ ([link][efficientnet_lite2_fp32_2])| + | +efficientnet_lite3_fp32_2.tflite|+ ([link][efficientnet_lite3_fp32_2])| + | +efficientnet_lite4_fp32_2.tflite|+ ([link][efficientnet_lite4_fp32_2])| + | +lite-efficientnet_lite0_uint8_2.tflite|+ ([link][efficientnet_lite0_uint8_2])| + | +lite-efficientnet_lite1_uint8_2.tflite|+ ([link][efficientnet_lite1_uint8_2])| + | +lite-efficientnet_lite2_uint8_2.tflite|+ ([link][efficientnet_lite2_uint8_2])| + | +lite-efficientnet_lite3_uint8_2.tflite|+ ([link][efficientnet_lite3_uint8_2])| + | +lite-efficientnet_lite4_uint8_2.tflite|+ ([link][efficientnet_lite4_uint8_2])| + | +efficientnet_lite0_int8_2.tflite|+ ([link][efficientnet_lite0_int8_2])| + | +efficientnet_lite1_int8_2.tflite|+ ([link][efficientnet_lite1_int8_2])| + | +efficientnet_lite2_int8_2.tflite|+ ([link][efficientnet_lite2_int8_2])| + | +efficientnet_lite3_int8_2.tflite|+ ([link][efficientnet_lite3_int8_2])| + | +efficientnet_lite4_int8_2.tflite|+ ([link][efficientnet_lite4_int8_2])| + | **Note:** inference implementation for EfficientNet-models supported for batch size that equals 1. diff --git a/results/validation/validation_results_tflite.md b/results/validation/validation_results_tflite.md index 558d582d2..5d2e55552 100644 --- a/results/validation/validation_results_tflite.md +++ b/results/validation/validation_results_tflite.md @@ -28,6 +28,22 @@ inception-resnet-v2-tf|Image resolution: 299x299.
Mean: [123.675,116.28,103.5 mobilenet-v3-small-1.0-224|-| 0.4481893 Granny Smith
0.0884615 lemon
0.0727510 pop bottle, soda bottle
0.0331238 saltshaker, salt shaker
0.0218442 pitcher, ewer| mobilenet-v3-large-1.0-224|-|0.6718515 Granny Smith
0.1939126 piggy bank, penny bank
0.0254287 lemon
0.0245753 vase
0.0090322 teapot| resnet-50-tf|Mean: [123.675,116.28,103.53].| 0.9553038 Granny Smith
0.0052123 lemon
0.0047185 piggy bank, penny bank
0.0045875 orange
0.0044233 necklace| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1806599 dumbbell
0.1265180 screw
0.0506000 Granny Smith
0.0467094 barbell
0.0345199 vase| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.0281865 wool, woolen, woollen
0.0269535 water bottle
0.0259669 trilobite
0.0250501 Granny Smith
0.0229548 teapot| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1039253 vase
0.1024296 pitcher, ewer
0.0350566 teapot
0.0325848 hook, claw
0.0292138 cup| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.5777039 Granny Smith
0.1232639 teapot
0.0563486 pitcher, ewer
0.0132013 jack-o'-lantern
0.0130741 orange| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| + #### Test image #2 @@ -53,6 +69,22 @@ inception-resnet-v2-tf|Image resolution: 299x299.
Mean: [123.675,116.28,103.5 mobilenet-v3-small-1.0-224|-|0.5813942 junco, snowbird
0.0588930 brambling, Fringilla montifringilla
0.0446762 house finch, linnet, Carpodacus mexicanus
0.0411857 goldfinch, Carduelis carduelis
0.0150912 chickadee| mobilenet-v3-large-1.0-224|-|0.7943738 junco, snowbird
0.0318200 brambling, Fringilla montifringilla
0.0084637 water ouzel, dipper
0.0071047 goldfinch, Carduelis carduelis
0.0061734 chickadee| resnet-50-tf|Mean: [123.675,116.28,103.53].|0.9983400 junco, snowbird
0.0004680 brambling, Fringilla montifringilla
0.0003848 chickadee
0.0003656 water ouzel, dipper
0.0003383 goldfinch, Carduelis carduelis| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.0890195 chickadee
0.0884429 bee eater
0.0799505 goldfinch, Carduelis carduelis
0.0760872 brambling, Fringilla montifringilla
0.0670157 hummingbird| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1486115 guenon, guenon monkey
0.0709516 three-toed sloth, ai, Bradypus tridactylus
0.0540262 marmoset
0.0501767 toucan
0.0370303 junco, snowbird| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.2191649 bulbul
0.2073708 three-toed sloth, ai, Bradypus tridactylus
0.1857240 chickadee
0.0258803 junco, snowbird
0.0256731 house finch, linnet, Carpodacus mexicanus| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.4806626 junco, snowbird
0.1389775 brambling, Fringilla montifringilla
0.1015250 house finch, linnet, Carpodacus mexicanus
0.0838527 chickadee
0.0206517 bulbul| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.3836534 chickadee
0.3339088 junco, snowbird
0.0034993 brambling, Fringilla montifringilla
0.0031002 water ouzel, dipper
0.0028740 hummingbird| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| + #### Test image #3 @@ -64,20 +96,35 @@ Image resolution: 333 x 500 -Model | Parameters | Python API | --|-|-| -densenet-121|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].| 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| -googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.1235979 breastplate, aegis, egis
0.1017586 lipstick, lip rouge
0.0949444 drum, membranophone, tympan
0.0817947 convertible
0.0486889 fire engine, fire truck| -googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.2662660 convertible
0.0966037 dogsled, dog sled, dog sleigh
0.0876837 breastplate, aegis, egis
0.0488674 beaker
0.0343599 drum, membranophone, tympan| -googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.4653829 beaker
0.3437532 breastplate, aegis, egis
0.0512180 suit, suit of clothes
0.0174647 lipstick, lip rouge
0.0134649 lighter, light, igniter, ignitor| -googlenet-v4-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.6737013 lifeboat
0.0432948 submarine, pigboat, sub, U-boat
0.0322841 fireboat
0.0264144 beacon, lighthouse, beacon light, pharos
0.0147488 drilling platform, offshore rig| -mobilenet-v1-1.0-224-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.1175058 lifeboat
0.1106691 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1055247 liner, ocean liner
0.0836357 beacon, lighthouse, beacon light, pharos
0.0784211 drilling platform, offshore rig| -mobilenet-v2-1.0-224|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.2761748 beacon, lighthouse, beacon light, pharos
0.1192475 liner, ocean liner
0.0864237 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0541655 drilling platform, offshore rig
0.0266723 container ship, containership, container vessel| -mobilenet-v2-1.4-224|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.2250047 beacon, lighthouse, beacon light, pharos
0.2051269 container ship, containership, container vessel
0.1319712 liner, ocean liner
0.0256291 dock, dockage, docking facility
0.0241968 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -inception-resnet-v2-tf|Image resolution: 299x299.
Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|6.4228325 breakwater, groin, groyne, mole, bulwark, seawall, jetty
6.0842223 liner, ocean liner
5.8280630 fireboat
5.7098336 dock, dockage, docking facility
5.6666737 container ship, containership, container vessel| -mobilenet-v3-small-1.0-224|-|0.0980954 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0957138 container ship, containership, container vessel
0.0853775 pirate, pirate ship
0.0690932 drilling platform, offshore rig
0.0685616 lifeboat| -mobilenet-v3-large-1.0-224|-|0.1806492 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1449896 lifeboat
0.1315165 submarine, pigboat, sub, U-boat
0.0884149 dock, dockage, docking facility
0.0476540 fireboat| -resnet-50-tf|Mean: [123.675,116.28,103.53].|0.2357713 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1480762 liner, ocean liner
0.1104688 container ship, containership, container vessel
0.1095407 drilling platform, offshore rig
0.0915569 beacon, lighthouse, beacon light, pharos| +Model | Parameters | Python API | +-|-----------------------------------------------------------------------------------------------------|-| +densenet-121| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. | 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| +googlenet-v1-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.1235979 breastplate, aegis, egis
0.1017586 lipstick, lip rouge
0.0949444 drum, membranophone, tympan
0.0817947 convertible
0.0486889 fire engine, fire truck| +googlenet-v2-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.2662660 convertible
0.0966037 dogsled, dog sled, dog sleigh
0.0876837 breastplate, aegis, egis
0.0488674 beaker
0.0343599 drum, membranophone, tympan| +googlenet-v3| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.4653829 beaker
0.3437532 breastplate, aegis, egis
0.0512180 suit, suit of clothes
0.0174647 lipstick, lip rouge
0.0134649 lighter, light, igniter, ignitor| +googlenet-v4-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.6737013 lifeboat
0.0432948 submarine, pigboat, sub, U-boat
0.0322841 fireboat
0.0264144 beacon, lighthouse, beacon light, pharos
0.0147488 drilling platform, offshore rig| +mobilenet-v1-1.0-224-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.1175058 lifeboat
0.1106691 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1055247 liner, ocean liner
0.0836357 beacon, lighthouse, beacon light, pharos
0.0784211 drilling platform, offshore rig| +mobilenet-v2-1.0-224| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.2761748 beacon, lighthouse, beacon light, pharos
0.1192475 liner, ocean liner
0.0864237 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0541655 drilling platform, offshore rig
0.0266723 container ship, containership, container vessel| +mobilenet-v2-1.4-224| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.2250047 beacon, lighthouse, beacon light, pharos
0.2051269 container ship, containership, container vessel
0.1319712 liner, ocean liner
0.0256291 dock, dockage, docking facility
0.0241968 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +inception-resnet-v2-tf| Image resolution: 299x299.
Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |6.4228325 breakwater, groin, groyne, mole, bulwark, seawall, jetty
6.0842223 liner, ocean liner
5.8280630 fireboat
5.7098336 dock, dockage, docking facility
5.6666737 container ship, containership, container vessel| +mobilenet-v3-small-1.0-224| - |0.0980954 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0957138 container ship, containership, container vessel
0.0853775 pirate, pirate ship
0.0690932 drilling platform, offshore rig
0.0685616 lifeboat| +mobilenet-v3-large-1.0-224| - |0.1806492 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1449896 lifeboat
0.1315165 submarine, pigboat, sub, U-boat
0.0884149 dock, dockage, docking facility
0.0476540 fireboat| +resnet-50-tf| Mean: [123.675,116.28,103.53]. |0.2357713 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1480762 liner, ocean liner
0.1104688 container ship, containership, container vessel
0.1095407 drilling platform, offshore rig
0.0915569 beacon, lighthouse, beacon light, pharos| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1718010 liner, ocean liner
0.1557053 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1440452 submarine, pigboat, sub, U-boat
0.0589084 beacon, lighthouse, beacon light, pharos
0.0228401 space shuttle| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1246776 beacon, lighthouse, beacon light, pharos
0.0895273 liner, ocean liner
0.0312974 aircraft carrier, carrier, flattop, attack aircraft carrier
0.0166320 submarine, pigboat, sub, U-boat
0.0145705 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.2495511 beacon, lighthouse, beacon light, pharos
0.1150195 submarine, pigboat, sub, U-boat
0.0678146 liner, ocean liner
0.0312395 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0109627 catamaran| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.0830593 liner, ocean liner
0.0747621 beacon, lighthouse, beacon light, pharos
0.0370711 container ship, containership, container vessel
0.0300901 sewing machine
0.0277146 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.9409395 liner, ocean liner
0.0027906 container ship, containership, container vessel
0.0024161 dock, dockage, docking facility
0.0019609 fireboat
0.0011747 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| ### Other tasks From b3f88fd3c1aa32a90166ec9e06117f4fd6c11744 Mon Sep 17 00:00:00 2001 From: igkonov Date: Tue, 10 Oct 2023 20:14:34 +0300 Subject: [PATCH 03/10] table fix --- results/tflite_models_checklist.md | 72 +++++++++++++++--------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/results/tflite_models_checklist.md b/results/tflite_models_checklist.md index e61d3e692..09858c611 100644 --- a/results/tflite_models_checklist.md +++ b/results/tflite_models_checklist.md @@ -6,18 +6,18 @@ Model | Availability in OMZ (2023.03.10) | Availability in the validation table | -|-|--------------------------------------| -densenet-121|+-| + | -googlenet-v1-tf|+-| + | -googlenet-v2-tf|+-| + | -googlenet-v3|+-| + | -googlenet-v4-tf|+-| + | -mobilenet-v1-1.0-224-tf|+-| + | -mobilenet-v2-1.0-224|+-| + | -mobilenet-v2-1.4-224|+-| + | -mobilenet-v3-small-1.0-224|+-| + | -mobilenet-v3-large-1.0-224|+-| + | -inception-resnet-v2-tf|+| + | -resnet-50-tf|+-| + | +densenet-121|+-| + | +googlenet-v1-tf|+-| + | +googlenet-v2-tf|+-| + | +googlenet-v3|+-| + | +googlenet-v4-tf|+-| + | +mobilenet-v1-1.0-224-tf|+-| + | +mobilenet-v2-1.0-224|+-| + | +mobilenet-v2-1.4-224|+-| + | +mobilenet-v3-small-1.0-224|+-| + | +mobilenet-v3-large-1.0-224|+-| + | +inception-resnet-v2-tf|+| + | +resnet-50-tf|+-| + | **Notes:** @@ -38,30 +38,30 @@ resnet-50-tf|+-| + | ### Image classification Model | Availability in TF hub (2023.03.10) | Availability in the validation table | --|-|--------------------------------------| -lite-model_mobilenet_v1_100_224_fp32_1.tflite|+ ([link][mobilenet_v1_100_224_fp32_1])| + | -lite-model_mobilenet_v2_100_224_fp32_1.tflite|+ ([link][mobilenet_v2_100_224_fp32_1])| + | -lite-model_mobilenet_v3_small_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_small_100_224_fp32_1])| + | -lite-model_mobilenet_v3_large_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_large_100_224_fp32_1])| + | -lite-model_mobilenet_v1_100_224_uint8_1.tflite|+ ([link][mobilenet_v1_100_224_uint8_1])| + | -lite-model_mobilenet_v2_100_224_uint8_1.tflite|+ ([link][mobilenet_v2_100_224_uint8_1])| + | -lite-model_mobilenet_v3_small_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_small_100_224_uint8_1])| + | -lite-model_mobilenet_v3_large_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_large_100_224_uint8_1])| + | -efficientnet_lite0_fp32_2.tflite|+ ([link][efficientnet_lite0_fp32_2])| + | -efficientnet_lite1_fp32_2.tflite|+ ([link][efficientnet_lite1_fp32_2])| + | -efficientnet_lite2_fp32_2.tflite|+ ([link][efficientnet_lite2_fp32_2])| + | -efficientnet_lite3_fp32_2.tflite|+ ([link][efficientnet_lite3_fp32_2])| + | -efficientnet_lite4_fp32_2.tflite|+ ([link][efficientnet_lite4_fp32_2])| + | -lite-efficientnet_lite0_uint8_2.tflite|+ ([link][efficientnet_lite0_uint8_2])| + | -lite-efficientnet_lite1_uint8_2.tflite|+ ([link][efficientnet_lite1_uint8_2])| + | -lite-efficientnet_lite2_uint8_2.tflite|+ ([link][efficientnet_lite2_uint8_2])| + | -lite-efficientnet_lite3_uint8_2.tflite|+ ([link][efficientnet_lite3_uint8_2])| + | -lite-efficientnet_lite4_uint8_2.tflite|+ ([link][efficientnet_lite4_uint8_2])| + | -efficientnet_lite0_int8_2.tflite|+ ([link][efficientnet_lite0_int8_2])| + | -efficientnet_lite1_int8_2.tflite|+ ([link][efficientnet_lite1_int8_2])| + | -efficientnet_lite2_int8_2.tflite|+ ([link][efficientnet_lite2_int8_2])| + | -efficientnet_lite3_int8_2.tflite|+ ([link][efficientnet_lite3_int8_2])| + | -efficientnet_lite4_int8_2.tflite|+ ([link][efficientnet_lite4_int8_2])| + | +-|-|------------------------------------| +lite-model_mobilenet_v1_100_224_fp32_1.tflite|+ ([link][mobilenet_v1_100_224_fp32_1])| + | +lite-model_mobilenet_v2_100_224_fp32_1.tflite|+ ([link][mobilenet_v2_100_224_fp32_1])| + | +lite-model_mobilenet_v3_small_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_small_100_224_fp32_1])| + | +lite-model_mobilenet_v3_large_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_large_100_224_fp32_1])| + | +lite-model_mobilenet_v1_100_224_uint8_1.tflite|+ ([link][mobilenet_v1_100_224_uint8_1])| + | +lite-model_mobilenet_v2_100_224_uint8_1.tflite|+ ([link][mobilenet_v2_100_224_uint8_1])| + | +lite-model_mobilenet_v3_small_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_small_100_224_uint8_1])| + | +lite-model_mobilenet_v3_large_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_large_100_224_uint8_1])| + | +efficientnet_lite0_fp32_2.tflite|+ ([link][efficientnet_lite0_fp32_2])| + | +efficientnet_lite1_fp32_2.tflite|+ ([link][efficientnet_lite1_fp32_2])| + | +efficientnet_lite2_fp32_2.tflite|+ ([link][efficientnet_lite2_fp32_2])| + | +efficientnet_lite3_fp32_2.tflite|+ ([link][efficientnet_lite3_fp32_2])| + | +efficientnet_lite4_fp32_2.tflite|+ ([link][efficientnet_lite4_fp32_2])| + | +lite-efficientnet_lite0_uint8_2.tflite|+ ([link][efficientnet_lite0_uint8_2])| + | +lite-efficientnet_lite1_uint8_2.tflite|+ ([link][efficientnet_lite1_uint8_2])| + | +lite-efficientnet_lite2_uint8_2.tflite|+ ([link][efficientnet_lite2_uint8_2])| + | +lite-efficientnet_lite3_uint8_2.tflite|+ ([link][efficientnet_lite3_uint8_2])| + | +lite-efficientnet_lite4_uint8_2.tflite|+ ([link][efficientnet_lite4_uint8_2])| + | +efficientnet_lite0_int8_2.tflite|+ ([link][efficientnet_lite0_int8_2])| + | +efficientnet_lite1_int8_2.tflite|+ ([link][efficientnet_lite1_int8_2])| + | +efficientnet_lite2_int8_2.tflite|+ ([link][efficientnet_lite2_int8_2])| + | +efficientnet_lite3_int8_2.tflite|+ ([link][efficientnet_lite3_int8_2])| + | +efficientnet_lite4_int8_2.tflite|+ ([link][efficientnet_lite4_int8_2])| + | **Note:** inference implementation for EfficientNet-models supported for batch size that equals 1. From a913987a84ab1e4132e8c0ce75724407d65ee8eb Mon Sep 17 00:00:00 2001 From: igkonov Date: Wed, 11 Oct 2023 20:48:27 +0300 Subject: [PATCH 04/10] googlenet-v1-tf,v2-tf,v3 labels fix --- results/tflite_models_checklist.md | 4 ++-- .../validation/validation_results_tflite.md | 18 +++++++++--------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/results/tflite_models_checklist.md b/results/tflite_models_checklist.md index 09858c611..1164ee090 100644 --- a/results/tflite_models_checklist.md +++ b/results/tflite_models_checklist.md @@ -5,7 +5,7 @@ ### Image classification Model | Availability in OMZ (2023.03.10) | Availability in the validation table | --|-|--------------------------------------| +-|-|-| densenet-121|+-| + | googlenet-v1-tf|+-| + | googlenet-v2-tf|+-| + | @@ -38,7 +38,7 @@ resnet-50-tf|+-| + | ### Image classification Model | Availability in TF hub (2023.03.10) | Availability in the validation table | --|-|------------------------------------| +-|-|-| lite-model_mobilenet_v1_100_224_fp32_1.tflite|+ ([link][mobilenet_v1_100_224_fp32_1])| + | lite-model_mobilenet_v2_100_224_fp32_1.tflite|+ ([link][mobilenet_v2_100_224_fp32_1])| + | lite-model_mobilenet_v3_small_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_small_100_224_fp32_1])| + | diff --git a/results/validation/validation_results_tflite.md b/results/validation/validation_results_tflite.md index 5d2e55552..bc58a6da0 100644 --- a/results/validation/validation_results_tflite.md +++ b/results/validation/validation_results_tflite.md @@ -17,9 +17,9 @@ Image resolution: 709 x 510 Model | Parameters | Python API | -|-|-| densenet-121|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.9523314 Granny Smith
0.0132282 orange
0.0125180 lemon
0.0027912 banana
0.0020333 piggy bank, penny bank| -googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.6735930 strawberry
0.0737855 pill bottle
0.0155380 vault
0.0154004 plane, carpenter's plane, woodworking plane
0.0136552 sandal| -googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9849940 strawberry
0.0010004 fig
0.0009706 hay
0.0006835 thatch, thatched roof
0.0006694 jackfruit, jak, jack| -googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9867674 strawberry
0.0008529 binder, ring-binder
0.0005354 pill bottle
0.0003701 hay
0.0001682 pop bottle, soda bottle| +googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.6735930 Granny Smith
0.0737855 piggy bank, penny bank
0.0155380 vase
0.0154004 pitcher, ewer
0.0136552 saltshaker, salt shaker| +googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9849940 Granny Smith
0.0010004 lemon
0.0009706 pomegranate
0.0006835 tennis ball
0.0006694 banana| +googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9867674 Granny Smith
0.0008529 bikini, two-piece
0.0005354 piggy bank, penny bank
0.0003701 pomegranate
0.0001682 pool table, billiard table, snooker table| googlenet-v4-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.9969806 Granny Smith
0.0001207 Rhodesian ridgeback
0.0000488 hair slide
0.0000473 pineapple, ananas
0.0000330 banana| mobilenet-v1-1.0-224-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.2949494 pitcher, ewer
0.1867124 saltshaker, salt shaker
0.1249271 necklace
0.0867643 piggy bank, penny bank
0.0360211 Granny Smith| mobilenet-v2-1.0-224|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.4164807 Granny Smith
0.3500757 piggy bank, penny bank
0.0358796 saltshaker, salt shaker
0.0147685 vase
0.0131548 pitcher, ewer| @@ -58,9 +58,9 @@ Image resolution: 500 x 500 Model | Parameters | Python API | -|-|-| densenet-121|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.9841611 junco, snowbird
0.0072198 chickadee
0.0034962 brambling, Fringilla montifringilla
0.0016226 water ouzel, dipper
0.0012858 indigo bunting, indigo finch, indigo bird, Passerina cyanea| -googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.7443183 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0474523 goldfinch, Carduelis carduelis
0.0457429 water ouzel, dipper
0.0213391 house finch, linnet, Carpodacus mexicanus
0.0085102 junco, snowbird| -googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9265908 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0166747 goldfinch, Carduelis carduelis
0.0058714 water ouzel, dipper
0.0026126 kite
0.0022344 robin, American robin, Turdus migratorius| -googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9488292 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0005887 kite
0.0004797 jack-o'-lantern
0.0003071 robin, American robin, Turdus migratorius
0.0002692 cliff dwelling| +googlenet-v1-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.7443183 junco, snowbird
0.0474523 brambling, Fringilla montifringilla
0.0457429 chickadee
0.0213391 goldfinch, Carduelis carduelis
0.0085102 house finch, linnet, Carpodacus mexicanus| +googlenet-v2-tf|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9265908 junco, snowbird
0.0166747 brambling, Fringilla montifringilla
0.0058714 chickadee
0.0026126 water ouzel, dipper
0.0022344 indigo bunting, indigo finch, indigo bird, Passerina cyanea| +googlenet-v3|Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.9488292 junco, snowbird
0.0005887 water ouzel, dipper
0.0004797 iron, smoothing iron
0.0003071 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0002692 cleaver, meat cleaver, chopper| googlenet-v4-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].| 0.9339716 junco, snowbird
0.0006892 chickadee
0.0005481 brambling, Fringilla montifringilla
0.0004948 indigo bunting, indigo finch, indigo bird, Passerina cyanea
0.0004539 water ouzel, dipper| mobilenet-v1-1.0-224-tf|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.7099941 junco, snowbird
0.2239839 chickadee
0.0195020 goldfinch, Carduelis carduelis
0.0140457 jay
0.0136091 brambling, Fringilla montifringilla| mobilenet-v2-1.0-224|Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.3981952 junco, snowbird
0.0649636 chickadee
0.0456628 brambling, Fringilla montifringilla
0.0063850 water ouzel, dipper
0.0041957 goldfinch, Carduelis carduelis| @@ -99,9 +99,9 @@ Image resolution: 333 x 500 Model | Parameters | Python API | -|-----------------------------------------------------------------------------------------------------|-| densenet-121| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. | 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| -googlenet-v1-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.1235979 breastplate, aegis, egis
0.1017586 lipstick, lip rouge
0.0949444 drum, membranophone, tympan
0.0817947 convertible
0.0486889 fire engine, fire truck| -googlenet-v2-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.2662660 convertible
0.0966037 dogsled, dog sled, dog sleigh
0.0876837 breastplate, aegis, egis
0.0488674 beaker
0.0343599 drum, membranophone, tympan| -googlenet-v3| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.4653829 beaker
0.3437532 breastplate, aegis, egis
0.0512180 suit, suit of clothes
0.0174647 lipstick, lip rouge
0.0134649 lighter, light, igniter, ignitor| +googlenet-v1-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.1235979 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1017586 liner, ocean liner
0.0949444 drilling platform, offshore rig
0.0817947 container ship, containership, container vessel
0.0486889 fireboat| +googlenet-v2-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.2662660 container ship, containership, container vessel
0.0966037 dock, dockage, docking facility
0.0876837 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0488674 beacon, lighthouse, beacon light, pharos
0.0343599 drilling platform, offshore rig| +googlenet-v3| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.4653829 beacon, lighthouse, beacon light, pharos
0.3437532 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0512180 submarine, pigboat, sub, U-boat
0.0174647 liner, ocean liner
0.0134649 lifeboat| googlenet-v4-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.6737013 lifeboat
0.0432948 submarine, pigboat, sub, U-boat
0.0322841 fireboat
0.0264144 beacon, lighthouse, beacon light, pharos
0.0147488 drilling platform, offshore rig| mobilenet-v1-1.0-224-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.1175058 lifeboat
0.1106691 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1055247 liner, ocean liner
0.0836357 beacon, lighthouse, beacon light, pharos
0.0784211 drilling platform, offshore rig| mobilenet-v2-1.0-224| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.2761748 beacon, lighthouse, beacon light, pharos
0.1192475 liner, ocean liner
0.0864237 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0541655 drilling platform, offshore rig
0.0266723 container ship, containership, container vessel| From 70d3a2ecf5d5b3d133d74960e9b7b21d9144d66e Mon Sep 17 00:00:00 2001 From: igkonov Date: Fri, 27 Oct 2023 13:31:23 +0300 Subject: [PATCH 05/10] table fix --- results/tflite_models_checklist.md | 24 ++-- .../validation/validation_results_tflite.md | 118 +++++++++--------- 2 files changed, 71 insertions(+), 71 deletions(-) diff --git a/results/tflite_models_checklist.md b/results/tflite_models_checklist.md index 1164ee090..28142866c 100644 --- a/results/tflite_models_checklist.md +++ b/results/tflite_models_checklist.md @@ -6,18 +6,18 @@ Model | Availability in OMZ (2023.03.10) | Availability in the validation table | -|-|-| -densenet-121|+-| + | -googlenet-v1-tf|+-| + | -googlenet-v2-tf|+-| + | -googlenet-v3|+-| + | -googlenet-v4-tf|+-| + | -mobilenet-v1-1.0-224-tf|+-| + | -mobilenet-v2-1.0-224|+-| + | -mobilenet-v2-1.4-224|+-| + | -mobilenet-v3-small-1.0-224|+-| + | -mobilenet-v3-large-1.0-224|+-| + | -inception-resnet-v2-tf|+| + | -resnet-50-tf|+-| + | +densenet-121|+-|+| +googlenet-v1-tf|+-|+| +googlenet-v2-tf|+-|+| +googlenet-v3|+-|+| +googlenet-v4-tf|+-|+| +mobilenet-v1-1.0-224-tf|+-|+| +mobilenet-v2-1.0-224|+-|+| +mobilenet-v2-1.4-224|+-|+| +mobilenet-v3-small-1.0-224|+-|+| +mobilenet-v3-large-1.0-224|+-|+| +inception-resnet-v2-tf|+|+| +resnet-50-tf|+-|+| **Notes:** diff --git a/results/validation/validation_results_tflite.md b/results/validation/validation_results_tflite.md index bc58a6da0..f0d341a43 100644 --- a/results/validation/validation_results_tflite.md +++ b/results/validation/validation_results_tflite.md @@ -28,21 +28,21 @@ inception-resnet-v2-tf|Image resolution: 299x299.
Mean: [123.675,116.28,103.5 mobilenet-v3-small-1.0-224|-| 0.4481893 Granny Smith
0.0884615 lemon
0.0727510 pop bottle, soda bottle
0.0331238 saltshaker, salt shaker
0.0218442 pitcher, ewer| mobilenet-v3-large-1.0-224|-|0.6718515 Granny Smith
0.1939126 piggy bank, penny bank
0.0254287 lemon
0.0245753 vase
0.0090322 teapot| resnet-50-tf|Mean: [123.675,116.28,103.53].| 0.9553038 Granny Smith
0.0052123 lemon
0.0047185 piggy bank, penny bank
0.0045875 orange
0.0044233 necklace| -efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1806599 dumbbell
0.1265180 screw
0.0506000 Granny Smith
0.0467094 barbell
0.0345199 vase| -efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.0281865 wool, woolen, woollen
0.0269535 water bottle
0.0259669 trilobite
0.0250501 Granny Smith
0.0229548 teapot| -efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1039253 vase
0.1024296 pitcher, ewer
0.0350566 teapot
0.0325848 hook, claw
0.0292138 cup| -efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer| -efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.5777039 Granny Smith
0.1232639 teapot
0.0563486 pitcher, ewer
0.0132013 jack-o'-lantern
0.0130741 orange| -lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| -lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| -lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| -lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| -lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| -efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| -efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| -efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| -efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| -efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1806599 dumbbell
0.1265180 screw
0.0506000 Granny Smith
0.0467094 barbell
0.0345199 vase| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0281865 wool, woolen, woollen
0.0269535 water bottle
0.0259669 trilobite
0.0250501 Granny Smith
0.0229548 teapot| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1039253 vase
0.1024296 pitcher, ewer
0.0350566 teapot
0.0325848 hook, claw
0.0292138 cup| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.5777039 Granny Smith
0.1232639 teapot
0.0563486 pitcher, ewer
0.0132013 jack-o'-lantern
0.0130741 orange| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| #### Test image #2 @@ -69,21 +69,21 @@ inception-resnet-v2-tf|Image resolution: 299x299.
Mean: [123.675,116.28,103.5 mobilenet-v3-small-1.0-224|-|0.5813942 junco, snowbird
0.0588930 brambling, Fringilla montifringilla
0.0446762 house finch, linnet, Carpodacus mexicanus
0.0411857 goldfinch, Carduelis carduelis
0.0150912 chickadee| mobilenet-v3-large-1.0-224|-|0.7943738 junco, snowbird
0.0318200 brambling, Fringilla montifringilla
0.0084637 water ouzel, dipper
0.0071047 goldfinch, Carduelis carduelis
0.0061734 chickadee| resnet-50-tf|Mean: [123.675,116.28,103.53].|0.9983400 junco, snowbird
0.0004680 brambling, Fringilla montifringilla
0.0003848 chickadee
0.0003656 water ouzel, dipper
0.0003383 goldfinch, Carduelis carduelis| -efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.0890195 chickadee
0.0884429 bee eater
0.0799505 goldfinch, Carduelis carduelis
0.0760872 brambling, Fringilla montifringilla
0.0670157 hummingbird| -efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1486115 guenon, guenon monkey
0.0709516 three-toed sloth, ai, Bradypus tridactylus
0.0540262 marmoset
0.0501767 toucan
0.0370303 junco, snowbird| -efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.2191649 bulbul
0.2073708 three-toed sloth, ai, Bradypus tridactylus
0.1857240 chickadee
0.0258803 junco, snowbird
0.0256731 house finch, linnet, Carpodacus mexicanus| -efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.4806626 junco, snowbird
0.1389775 brambling, Fringilla montifringilla
0.1015250 house finch, linnet, Carpodacus mexicanus
0.0838527 chickadee
0.0206517 bulbul| -efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.3836534 chickadee
0.3339088 junco, snowbird
0.0034993 brambling, Fringilla montifringilla
0.0031002 water ouzel, dipper
0.0028740 hummingbird| -lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| -lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| -lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| -lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| -lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| -efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| -efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| -efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| -efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| -efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0890195 chickadee
0.0884429 bee eater
0.0799505 goldfinch, Carduelis carduelis
0.0760872 brambling, Fringilla montifringilla
0.0670157 hummingbird| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1486115 guenon, guenon monkey
0.0709516 three-toed sloth, ai, Bradypus tridactylus
0.0540262 marmoset
0.0501767 toucan
0.0370303 junco, snowbird| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.2191649 bulbul
0.2073708 three-toed sloth, ai, Bradypus tridactylus
0.1857240 chickadee
0.0258803 junco, snowbird
0.0256731 house finch, linnet, Carpodacus mexicanus| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.4806626 junco, snowbird
0.1389775 brambling, Fringilla montifringilla
0.1015250 house finch, linnet, Carpodacus mexicanus
0.0838527 chickadee
0.0206517 bulbul| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.3836534 chickadee
0.3339088 junco, snowbird
0.0034993 brambling, Fringilla montifringilla
0.0031002 water ouzel, dipper
0.0028740 hummingbird| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| #### Test image #3 @@ -96,35 +96,35 @@ Image resolution: 333 x 500 -Model | Parameters | Python API | --|-----------------------------------------------------------------------------------------------------|-| -densenet-121| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. | 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| -googlenet-v1-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.1235979 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1017586 liner, ocean liner
0.0949444 drilling platform, offshore rig
0.0817947 container ship, containership, container vessel
0.0486889 fireboat| -googlenet-v2-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.2662660 container ship, containership, container vessel
0.0966037 dock, dockage, docking facility
0.0876837 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0488674 beacon, lighthouse, beacon light, pharos
0.0343599 drilling platform, offshore rig| -googlenet-v3| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5]. |0.4653829 beacon, lighthouse, beacon light, pharos
0.3437532 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0512180 submarine, pigboat, sub, U-boat
0.0174647 liner, ocean liner
0.0134649 lifeboat| -googlenet-v4-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.6737013 lifeboat
0.0432948 submarine, pigboat, sub, U-boat
0.0322841 fireboat
0.0264144 beacon, lighthouse, beacon light, pharos
0.0147488 drilling platform, offshore rig| -mobilenet-v1-1.0-224-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.1175058 lifeboat
0.1106691 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1055247 liner, ocean liner
0.0836357 beacon, lighthouse, beacon light, pharos
0.0784211 drilling platform, offshore rig| -mobilenet-v2-1.0-224| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.2761748 beacon, lighthouse, beacon light, pharos
0.1192475 liner, ocean liner
0.0864237 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0541655 drilling platform, offshore rig
0.0266723 container ship, containership, container vessel| -mobilenet-v2-1.4-224| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |0.2250047 beacon, lighthouse, beacon light, pharos
0.2051269 container ship, containership, container vessel
0.1319712 liner, ocean liner
0.0256291 dock, dockage, docking facility
0.0241968 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -inception-resnet-v2-tf| Image resolution: 299x299.
Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375]. |6.4228325 breakwater, groin, groyne, mole, bulwark, seawall, jetty
6.0842223 liner, ocean liner
5.8280630 fireboat
5.7098336 dock, dockage, docking facility
5.6666737 container ship, containership, container vessel| -mobilenet-v3-small-1.0-224| - |0.0980954 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0957138 container ship, containership, container vessel
0.0853775 pirate, pirate ship
0.0690932 drilling platform, offshore rig
0.0685616 lifeboat| -mobilenet-v3-large-1.0-224| - |0.1806492 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1449896 lifeboat
0.1315165 submarine, pigboat, sub, U-boat
0.0884149 dock, dockage, docking facility
0.0476540 fireboat| -resnet-50-tf| Mean: [123.675,116.28,103.53]. |0.2357713 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1480762 liner, ocean liner
0.1104688 container ship, containership, container vessel
0.1095407 drilling platform, offshore rig
0.0915569 beacon, lighthouse, beacon light, pharos| -efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1718010 liner, ocean liner
0.1557053 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1440452 submarine, pigboat, sub, U-boat
0.0589084 beacon, lighthouse, beacon light, pharos
0.0228401 space shuttle| -efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.1246776 beacon, lighthouse, beacon light, pharos
0.0895273 liner, ocean liner
0.0312974 aircraft carrier, carrier, flattop, attack aircraft carrier
0.0166320 submarine, pigboat, sub, U-boat
0.0145705 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.2495511 beacon, lighthouse, beacon light, pharos
0.1150195 submarine, pigboat, sub, U-boat
0.0678146 liner, ocean liner
0.0312395 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0109627 catamaran| -efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.0830593 liner, ocean liner
0.0747621 beacon, lighthouse, beacon light, pharos
0.0370711 container ship, containership, container vessel
0.0300901 sewing machine
0.0277146 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |0.9409395 liner, ocean liner
0.0027906 container ship, containership, container vessel
0.0024161 dock, dockage, docking facility
0.0019609 fireboat
0.0011747 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| -lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| -lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| -lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| -lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| -efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| -efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| -efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| -efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| -efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0]. |68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| +Model | Parameters | Python API | +-|---------------------------------------------------------------------------------------------------|-| +densenet-121| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].| 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| +googlenet-v1-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.1235979 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1017586 liner, ocean liner
0.0949444 drilling platform, offshore rig
0.0817947 container ship, containership, container vessel
0.0486889 fireboat| +googlenet-v2-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.2662660 container ship, containership, container vessel
0.0966037 dock, dockage, docking facility
0.0876837 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0488674 beacon, lighthouse, beacon light, pharos
0.0343599 drilling platform, offshore rig| +googlenet-v3| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.4653829 beacon, lighthouse, beacon light, pharos
0.3437532 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0512180 submarine, pigboat, sub, U-boat
0.0174647 liner, ocean liner
0.0134649 lifeboat| +googlenet-v4-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.6737013 lifeboat
0.0432948 submarine, pigboat, sub, U-boat
0.0322841 fireboat
0.0264144 beacon, lighthouse, beacon light, pharos
0.0147488 drilling platform, offshore rig| +mobilenet-v1-1.0-224-tf| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.1175058 lifeboat
0.1106691 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1055247 liner, ocean liner
0.0836357 beacon, lighthouse, beacon light, pharos
0.0784211 drilling platform, offshore rig| +mobilenet-v2-1.0-224| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.2761748 beacon, lighthouse, beacon light, pharos
0.1192475 liner, ocean liner
0.0864237 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0541655 drilling platform, offshore rig
0.0266723 container ship, containership, container vessel| +mobilenet-v2-1.4-224| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|0.2250047 beacon, lighthouse, beacon light, pharos
0.2051269 container ship, containership, container vessel
0.1319712 liner, ocean liner
0.0256291 dock, dockage, docking facility
0.0241968 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +inception-resnet-v2-tf| Image resolution: 299x299.
Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].|6.4228325 breakwater, groin, groyne, mole, bulwark, seawall, jetty
6.0842223 liner, ocean liner
5.8280630 fireboat
5.7098336 dock, dockage, docking facility
5.6666737 container ship, containership, container vessel| +mobilenet-v3-small-1.0-224|-|0.0980954 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0957138 container ship, containership, container vessel
0.0853775 pirate, pirate ship
0.0690932 drilling platform, offshore rig
0.0685616 lifeboat| +mobilenet-v3-large-1.0-224|-|0.1806492 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1449896 lifeboat
0.1315165 submarine, pigboat, sub, U-boat
0.0884149 dock, dockage, docking facility
0.0476540 fireboat| +resnet-50-tf| Mean: [123.675,116.28,103.53].|0.2357713 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1480762 liner, ocean liner
0.1104688 container ship, containership, container vessel
0.1095407 drilling platform, offshore rig
0.0915569 beacon, lighthouse, beacon light, pharos| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1718010 liner, ocean liner
0.1557053 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1440452 submarine, pigboat, sub, U-boat
0.0589084 beacon, lighthouse, beacon light, pharos
0.0228401 space shuttle| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1246776 beacon, lighthouse, beacon light, pharos
0.0895273 liner, ocean liner
0.0312974 aircraft carrier, carrier, flattop, attack aircraft carrier
0.0166320 submarine, pigboat, sub, U-boat
0.0145705 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.2495511 beacon, lighthouse, beacon light, pharos
0.1150195 submarine, pigboat, sub, U-boat
0.0678146 liner, ocean liner
0.0312395 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0109627 catamaran| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0830593 liner, ocean liner
0.0747621 beacon, lighthouse, beacon light, pharos
0.0370711 container ship, containership, container vessel
0.0300901 sewing machine
0.0277146 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.9409395 liner, ocean liner
0.0027906 container ship, containership, container vessel
0.0024161 dock, dockage, docking facility
0.0019609 fireboat
0.0011747 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| ### Other tasks From a83c685370c38d18f607632476832677fa8406f1 Mon Sep 17 00:00:00 2001 From: igkonov Date: Fri, 27 Oct 2023 14:16:52 +0300 Subject: [PATCH 06/10] table fix 3 --- results/tflite_models_checklist.md | 46 +++--- .../validation/validation_results_tflite.md | 138 ++++++------------ 2 files changed, 71 insertions(+), 113 deletions(-) diff --git a/results/tflite_models_checklist.md b/results/tflite_models_checklist.md index 28142866c..70c666866 100644 --- a/results/tflite_models_checklist.md +++ b/results/tflite_models_checklist.md @@ -39,29 +39,29 @@ resnet-50-tf|+-|+| Model | Availability in TF hub (2023.03.10) | Availability in the validation table | -|-|-| -lite-model_mobilenet_v1_100_224_fp32_1.tflite|+ ([link][mobilenet_v1_100_224_fp32_1])| + | -lite-model_mobilenet_v2_100_224_fp32_1.tflite|+ ([link][mobilenet_v2_100_224_fp32_1])| + | -lite-model_mobilenet_v3_small_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_small_100_224_fp32_1])| + | -lite-model_mobilenet_v3_large_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_large_100_224_fp32_1])| + | -lite-model_mobilenet_v1_100_224_uint8_1.tflite|+ ([link][mobilenet_v1_100_224_uint8_1])| + | -lite-model_mobilenet_v2_100_224_uint8_1.tflite|+ ([link][mobilenet_v2_100_224_uint8_1])| + | -lite-model_mobilenet_v3_small_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_small_100_224_uint8_1])| + | -lite-model_mobilenet_v3_large_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_large_100_224_uint8_1])| + | -efficientnet_lite0_fp32_2.tflite|+ ([link][efficientnet_lite0_fp32_2])| + | -efficientnet_lite1_fp32_2.tflite|+ ([link][efficientnet_lite1_fp32_2])| + | -efficientnet_lite2_fp32_2.tflite|+ ([link][efficientnet_lite2_fp32_2])| + | -efficientnet_lite3_fp32_2.tflite|+ ([link][efficientnet_lite3_fp32_2])| + | -efficientnet_lite4_fp32_2.tflite|+ ([link][efficientnet_lite4_fp32_2])| + | -lite-efficientnet_lite0_uint8_2.tflite|+ ([link][efficientnet_lite0_uint8_2])| + | -lite-efficientnet_lite1_uint8_2.tflite|+ ([link][efficientnet_lite1_uint8_2])| + | -lite-efficientnet_lite2_uint8_2.tflite|+ ([link][efficientnet_lite2_uint8_2])| + | -lite-efficientnet_lite3_uint8_2.tflite|+ ([link][efficientnet_lite3_uint8_2])| + | -lite-efficientnet_lite4_uint8_2.tflite|+ ([link][efficientnet_lite4_uint8_2])| + | -efficientnet_lite0_int8_2.tflite|+ ([link][efficientnet_lite0_int8_2])| + | -efficientnet_lite1_int8_2.tflite|+ ([link][efficientnet_lite1_int8_2])| + | -efficientnet_lite2_int8_2.tflite|+ ([link][efficientnet_lite2_int8_2])| + | -efficientnet_lite3_int8_2.tflite|+ ([link][efficientnet_lite3_int8_2])| + | -efficientnet_lite4_int8_2.tflite|+ ([link][efficientnet_lite4_int8_2])| + | +lite-model_mobilenet_v1_100_224_fp32_1.tflite|+ ([link][mobilenet_v1_100_224_fp32_1])|+| +lite-model_mobilenet_v2_100_224_fp32_1.tflite|+ ([link][mobilenet_v2_100_224_fp32_1])|+| +lite-model_mobilenet_v3_small_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_small_100_224_fp32_1])|+| +lite-model_mobilenet_v3_large_100_224_fp32_1.tflite|+ ([link][mobilenet_v3_large_100_224_fp32_1])|+| +lite-model_mobilenet_v1_100_224_uint8_1.tflite|+ ([link][mobilenet_v1_100_224_uint8_1])|+| +lite-model_mobilenet_v2_100_224_uint8_1.tflite|+ ([link][mobilenet_v2_100_224_uint8_1])|+| +lite-model_mobilenet_v3_small_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_small_100_224_uint8_1])|+| +lite-model_mobilenet_v3_large_100_224_uint8_1.tflite|+ ([link][mobilenet_v3_large_100_224_uint8_1])|+| +efficientnet_lite0_fp32_2.tflite|+ ([link][efficientnet_lite0_fp32_2])|+| +efficientnet_lite1_fp32_2.tflite|+ ([link][efficientnet_lite1_fp32_2])|+| +efficientnet_lite2_fp32_2.tflite|+ ([link][efficientnet_lite2_fp32_2])|+| +efficientnet_lite3_fp32_2.tflite|+ ([link][efficientnet_lite3_fp32_2])|+| +efficientnet_lite4_fp32_2.tflite|+ ([link][efficientnet_lite4_fp32_2])|+| +lite-efficientnet_lite0_uint8_2.tflite|+ ([link][efficientnet_lite0_uint8_2])|+| +lite-efficientnet_lite1_uint8_2.tflite|+ ([link][efficientnet_lite1_uint8_2])|+| +lite-efficientnet_lite2_uint8_2.tflite|+ ([link][efficientnet_lite2_uint8_2])|+| +lite-efficientnet_lite3_uint8_2.tflite|+ ([link][efficientnet_lite3_uint8_2])|+| +lite-efficientnet_lite4_uint8_2.tflite|+ ([link][efficientnet_lite4_uint8_2])|+| +efficientnet_lite0_int8_2.tflite|+ ([link][efficientnet_lite0_int8_2])|+| +efficientnet_lite1_int8_2.tflite|+ ([link][efficientnet_lite1_int8_2])|+| +efficientnet_lite2_int8_2.tflite|+ ([link][efficientnet_lite2_int8_2])|+| +efficientnet_lite3_int8_2.tflite|+ ([link][efficientnet_lite3_int8_2])|+| +efficientnet_lite4_int8_2.tflite|+ ([link][efficientnet_lite4_int8_2])|+| **Note:** inference implementation for EfficientNet-models supported for batch size that equals 1. diff --git a/results/validation/validation_results_tflite.md b/results/validation/validation_results_tflite.md index f0d341a43..f8fc71b98 100644 --- a/results/validation/validation_results_tflite.md +++ b/results/validation/validation_results_tflite.md @@ -28,21 +28,7 @@ inception-resnet-v2-tf|Image resolution: 299x299.
Mean: [123.675,116.28,103.5 mobilenet-v3-small-1.0-224|-| 0.4481893 Granny Smith
0.0884615 lemon
0.0727510 pop bottle, soda bottle
0.0331238 saltshaker, salt shaker
0.0218442 pitcher, ewer| mobilenet-v3-large-1.0-224|-|0.6718515 Granny Smith
0.1939126 piggy bank, penny bank
0.0254287 lemon
0.0245753 vase
0.0090322 teapot| resnet-50-tf|Mean: [123.675,116.28,103.53].| 0.9553038 Granny Smith
0.0052123 lemon
0.0047185 piggy bank, penny bank
0.0045875 orange
0.0044233 necklace| -efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1806599 dumbbell
0.1265180 screw
0.0506000 Granny Smith
0.0467094 barbell
0.0345199 vase| -efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0281865 wool, woolen, woollen
0.0269535 water bottle
0.0259669 trilobite
0.0250501 Granny Smith
0.0229548 teapot| -efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1039253 vase
0.1024296 pitcher, ewer
0.0350566 teapot
0.0325848 hook, claw
0.0292138 cup| -efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer| -efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.5777039 Granny Smith
0.1232639 teapot
0.0563486 pitcher, ewer
0.0132013 jack-o'-lantern
0.0130741 orange| -lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| -lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| -lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| -lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| -lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| -efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| -efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| -efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| -efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| -efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| + #### Test image #2 @@ -69,21 +55,6 @@ inception-resnet-v2-tf|Image resolution: 299x299.
Mean: [123.675,116.28,103.5 mobilenet-v3-small-1.0-224|-|0.5813942 junco, snowbird
0.0588930 brambling, Fringilla montifringilla
0.0446762 house finch, linnet, Carpodacus mexicanus
0.0411857 goldfinch, Carduelis carduelis
0.0150912 chickadee| mobilenet-v3-large-1.0-224|-|0.7943738 junco, snowbird
0.0318200 brambling, Fringilla montifringilla
0.0084637 water ouzel, dipper
0.0071047 goldfinch, Carduelis carduelis
0.0061734 chickadee| resnet-50-tf|Mean: [123.675,116.28,103.53].|0.9983400 junco, snowbird
0.0004680 brambling, Fringilla montifringilla
0.0003848 chickadee
0.0003656 water ouzel, dipper
0.0003383 goldfinch, Carduelis carduelis| -efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0890195 chickadee
0.0884429 bee eater
0.0799505 goldfinch, Carduelis carduelis
0.0760872 brambling, Fringilla montifringilla
0.0670157 hummingbird| -efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1486115 guenon, guenon monkey
0.0709516 three-toed sloth, ai, Bradypus tridactylus
0.0540262 marmoset
0.0501767 toucan
0.0370303 junco, snowbird| -efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.2191649 bulbul
0.2073708 three-toed sloth, ai, Bradypus tridactylus
0.1857240 chickadee
0.0258803 junco, snowbird
0.0256731 house finch, linnet, Carpodacus mexicanus| -efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.4806626 junco, snowbird
0.1389775 brambling, Fringilla montifringilla
0.1015250 house finch, linnet, Carpodacus mexicanus
0.0838527 chickadee
0.0206517 bulbul| -efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.3836534 chickadee
0.3339088 junco, snowbird
0.0034993 brambling, Fringilla montifringilla
0.0031002 water ouzel, dipper
0.0028740 hummingbird| -lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| -lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| -lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| -lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| -lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| -efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| -efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| -efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| -efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| -efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| #### Test image #3 @@ -110,21 +81,6 @@ inception-resnet-v2-tf| Image resolution: 299x299.
Mean: [123.675,116.28,103. mobilenet-v3-small-1.0-224|-|0.0980954 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0957138 container ship, containership, container vessel
0.0853775 pirate, pirate ship
0.0690932 drilling platform, offshore rig
0.0685616 lifeboat| mobilenet-v3-large-1.0-224|-|0.1806492 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1449896 lifeboat
0.1315165 submarine, pigboat, sub, U-boat
0.0884149 dock, dockage, docking facility
0.0476540 fireboat| resnet-50-tf| Mean: [123.675,116.28,103.53].|0.2357713 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1480762 liner, ocean liner
0.1104688 container ship, containership, container vessel
0.1095407 drilling platform, offshore rig
0.0915569 beacon, lighthouse, beacon light, pharos| -efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1718010 liner, ocean liner
0.1557053 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1440452 submarine, pigboat, sub, U-boat
0.0589084 beacon, lighthouse, beacon light, pharos
0.0228401 space shuttle| -efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1246776 beacon, lighthouse, beacon light, pharos
0.0895273 liner, ocean liner
0.0312974 aircraft carrier, carrier, flattop, attack aircraft carrier
0.0166320 submarine, pigboat, sub, U-boat
0.0145705 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.2495511 beacon, lighthouse, beacon light, pharos
0.1150195 submarine, pigboat, sub, U-boat
0.0678146 liner, ocean liner
0.0312395 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0109627 catamaran| -efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0830593 liner, ocean liner
0.0747621 beacon, lighthouse, beacon light, pharos
0.0370711 container ship, containership, container vessel
0.0300901 sewing machine
0.0277146 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.9409395 liner, ocean liner
0.0027906 container ship, containership, container vessel
0.0024161 dock, dockage, docking facility
0.0019609 fireboat
0.0011747 breakwater, groin, groyne, mole, bulwark, seawall, jetty| -lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| -lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| -lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| -lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| -lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| -efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| -efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| -efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| -efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| -efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| ### Other tasks @@ -154,21 +110,21 @@ mobilenet_v1_100_224_uint8_1|-|248.0000000 Granny Smith
1.0000000 lemon
0. mobilenet_v2_100_224_uint8_1|-|242.0000000 Granny Smith
2.0000000 piggy bank, penny bank
1.0000000 lemon
1.0000000 teapot
1.0000000 saltshaker, salt shaker| mobilenet_v3_small_100_224_uint8_1|-|25.0000000 junco, snowbird
13.0000000 water ouzel, dipper
11.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
7.0000000 goldfinch, Carduelis carduelis| mobilenet_v3_large_100_224_uint8_1|-|204.0000000 Granny Smith
6.0000000 piggy bank, penny bank
1.0000000 orange
1.0000000 teapot
1.0000000 vase| -efficientnet_lite0_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite0_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite0_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1806599 dumbbell
0.1265180 screw
0.0506000 Granny Smith
0.0467094 barbell
0.0345199 vase| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0281865 wool, woolen, woollen
0.0269535 water bottle
0.0259669 trilobite
0.0250501 Granny Smith
0.0229548 teapot| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1039253 vase
0.1024296 pitcher, ewer
0.0350566 teapot
0.0325848 hook, claw
0.0292138 cup| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer0.1926491 pitcher, ewer
0.1502650 teapot
0.1470646 electric fan, blower
0.1428124 water jug
0.1257372 strainer| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.5777039 Granny Smith
0.1232639 teapot
0.0563486 pitcher, ewer
0.0132013 jack-o'-lantern
0.0130741 orange| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|81.0000000 Granny Smith
30.0000000 bell pepper
27.0000000 orange
17.0000000 pomegranate
10.0000000 strawberry| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|25.0000000 bell pepper
20.0000000 Granny Smith
18.0000000 orange
16.0000000 candle, taper, wax light
11.0000000 piggy bank, penny bank| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|39.0000000 Granny Smith
27.0000000 orange
22.0000000 piggy bank, penny bank
13.0000000 bell pepper
12.0000000 candle, taper, wax light| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|207.0000000 Granny Smith
10.0000000 tennis ball
9.0000000 orange
4.0000000 piggy bank, penny bank
3.0000000 lemon| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|141.0000000 Granny Smith
19.0000000 orange
5.0000000 piggy bank, penny bank
4.0000000 banana
3.0000000 jack-o'-lantern| #### Test image #2 @@ -190,21 +146,22 @@ mobilenet_v1_100_224_uint8_1|-|181.0000000 junco, snowbird
20.0000000 brambli mobilenet_v2_100_224_uint8_1|-|221.0000000 junco, snowbird
6.0000000 chickadee
3.0000000 brambling, Fringilla montifringilla
1.0000000 water ouzel, dipper
0.0000000 toilet tissue, toilet paper, bathroom tissue| mobilenet_v3_small_100_224_uint8_1|-| 97.0000000 Granny Smith
27.0000000 tennis ball
8.0000000 ping-pong ball
5.0000000 candle, taper, wax light
5.0000000 saltshaker, salt shaker| mobilenet_v3_large_100_224_uint8_1|-|144.0000000 junco, snowbird
9.0000000 goldfinch, Carduelis carduelis
6.0000000 water ouzel, dipper
5.0000000 chickadee
5.0000000 brambling, Fringilla montifringilla| -efficientnet_lite0_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite0_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite0_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0890195 chickadee
0.0884429 bee eater
0.0799505 goldfinch, Carduelis carduelis
0.0760872 brambling, Fringilla montifringilla
0.0670157 hummingbird| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1486115 guenon, guenon monkey
0.0709516 three-toed sloth, ai, Bradypus tridactylus
0.0540262 marmoset
0.0501767 toucan
0.0370303 junco, snowbird| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.2191649 bulbul
0.2073708 three-toed sloth, ai, Bradypus tridactylus
0.1857240 chickadee
0.0258803 junco, snowbird
0.0256731 house finch, linnet, Carpodacus mexicanus| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.4806626 junco, snowbird
0.1389775 brambling, Fringilla montifringilla
0.1015250 house finch, linnet, Carpodacus mexicanus
0.0838527 chickadee
0.0206517 bulbul| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.3836534 chickadee
0.3339088 junco, snowbird
0.0034993 brambling, Fringilla montifringilla
0.0031002 water ouzel, dipper
0.0028740 hummingbird| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|38.0000000 bee eater
31.0000000 chickadee
31.0000000 house finch, linnet, Carpodacus mexicanus
29.0000000 junco, snowbird
24.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|84.0000000 jay
28.0000000 junco, snowbird
11.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
9.0000000 chickadee
7.0000000 little blue heron, Egretta caerulea| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|73.0000000 junco, snowbird
26.0000000 chickadee
8.0000000 brambling, Fringilla montifringilla
4.0000000 bulbul
3.0000000 jay| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|58.0000000 jay
26.0000000 indigo bunting, indigo finch, indigo bird, Passerina cyanea
20.0000000 chickadee
17.0000000 jacamar
11.0000000 bee eater| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|150.0000000 hummingbird
20.0000000 junco, snowbird
11.0000000 jacamar
6.0000000 water ouzel, dipper
4.0000000 chickadee| + #### Test image #3 @@ -226,21 +183,22 @@ mobilenet_v1_100_224_uint8_1|-|34.0000000 catamaran
26.0000000 liner, ocean l mobilenet_v2_100_224_uint8_1|-|15.0000000 pirate, pirate ship
14.0000000 liner, ocean liner
7.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
7.0000000 container ship, containership, container vessel
6.0000000 catamaran| mobilenet_v3_small_100_224_uint8_1|-|25.0000000 drilling platform, offshore rig
17.0000000 beacon, lighthouse, beacon light, pharos
15.0000000 aircraft carrier, carrier, flattop, attack aircraft carrier
12.0000000 container ship, containership, container vessel
12.0000000 sandbar, sand bar| mobilenet_v3_large_100_224_uint8_1|-|67.0000000 beacon, lighthouse, beacon light, pharos
40.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
32.0000000 container ship, containership, container vessel
10.0000000 fireboat
9.0000000 seashore, coast, seacoast, sea-coast| -efficientnet_lite0_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_fp32_2|Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite0_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_int8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite0_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite1_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite2_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite3_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| -efficientnet_lite4_uint8_1|Image resolution: 300x300.
Mean: [127,127,127].
Input scale: [128,128,128].|-| +efficientnet_lite0_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1718010 liner, ocean liner
0.1557053 aircraft carrier, carrier, flattop, attack aircraft carrier
0.1440452 submarine, pigboat, sub, U-boat
0.0589084 beacon, lighthouse, beacon light, pharos
0.0228401 space shuttle| +efficientnet_lite1_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.1246776 beacon, lighthouse, beacon light, pharos
0.0895273 liner, ocean liner
0.0312974 aircraft carrier, carrier, flattop, attack aircraft carrier
0.0166320 submarine, pigboat, sub, U-boat
0.0145705 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +efficientnet_lite2_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.2495511 beacon, lighthouse, beacon light, pharos
0.1150195 submarine, pigboat, sub, U-boat
0.0678146 liner, ocean liner
0.0312395 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0109627 catamaran| +efficientnet_lite3_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.0830593 liner, ocean liner
0.0747621 beacon, lighthouse, beacon light, pharos
0.0370711 container ship, containership, container vessel
0.0300901 sewing machine
0.0277146 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +efficientnet_lite4_fp32_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|0.9409395 liner, ocean liner
0.0027906 container ship, containership, container vessel
0.0024161 dock, dockage, docking facility
0.0019609 fireboat
0.0011747 breakwater, groin, groyne, mole, bulwark, seawall, jetty| +lite-efficientnet_lite0_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| +lite-efficientnet_lite1_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| +lite-efficientnet_lite2_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| +lite-efficientnet_lite3_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| +lite-efficientnet_lite4_uint8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| +efficientnet_lite0_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|53.0000000 lifeboat
34.0000000 catamaran
23.0000000 speedboat
15.0000000 liner, ocean liner
12.0000000 fireboat| +efficientnet_lite1_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|12.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
11.0000000 beacon, lighthouse, beacon light, pharos
10.0000000 stupa, tope
9.0000000 liner, ocean liner
8.0000000 catamaran| +efficientnet_lite2_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|19.0000000 liner, ocean liner
11.0000000 fireboat
10.0000000 beacon, lighthouse, beacon light, pharos
9.0000000 dock, dockage, docking facility
9.0000000 drilling platform, offshore rig| +efficientnet_lite3_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|47.0000000 beacon, lighthouse, beacon light, pharos
33.0000000 liner, ocean liner
15.0000000 breakwater, groin, groyne, mole, bulwark, seawall, jetty
8.0000000 container ship, containership, container vessel
7.0000000 lifeboat| +efficientnet_lite4_int8_2.tflite| Mean: [127.0, 127.0, 127.0].
Input scale: [128.0].|68.0000000 drilling platform, offshore rig
38.0000000 container ship, containership, container vessel
14.0000000 lifeboat
13.0000000 fireboat
12.0000000 dock, dockage, docking facility| + ### Other tasks From 029fefaff858d9e0a62e13ca79d518d64d2a4b31 Mon Sep 17 00:00:00 2001 From: igkonov Date: Fri, 27 Oct 2023 14:33:02 +0300 Subject: [PATCH 07/10] table fix 4 --- results/validation/validation_results_tflite.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/results/validation/validation_results_tflite.md b/results/validation/validation_results_tflite.md index f8fc71b98..8721a847f 100644 --- a/results/validation/validation_results_tflite.md +++ b/results/validation/validation_results_tflite.md @@ -67,8 +67,8 @@ Image resolution: 333 x 500 -Model | Parameters | Python API | --|---------------------------------------------------------------------------------------------------|-| +Model | Parameters | Python API | +-|-|-| densenet-121| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].| 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| googlenet-v1-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.1235979 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1017586 liner, ocean liner
0.0949444 drilling platform, offshore rig
0.0817947 container ship, containership, container vessel
0.0486889 fireboat| googlenet-v2-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.2662660 container ship, containership, container vessel
0.0966037 dock, dockage, docking facility
0.0876837 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.0488674 beacon, lighthouse, beacon light, pharos
0.0343599 drilling platform, offshore rig| From e49bb23dde54541e80eec90f414ae78d99c41a45 Mon Sep 17 00:00:00 2001 From: igkonov Date: Fri, 27 Oct 2023 14:35:05 +0300 Subject: [PATCH 08/10] table fix 4.1 --- results/validation/validation_results_tflite.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/results/validation/validation_results_tflite.md b/results/validation/validation_results_tflite.md index 8721a847f..0fdc38a31 100644 --- a/results/validation/validation_results_tflite.md +++ b/results/validation/validation_results_tflite.md @@ -67,7 +67,7 @@ Image resolution: 333 x 500 -Model | Parameters | Python API | +Model | Parameters | Python API | -|-|-| densenet-121| Mean: [123.675,116.28,103.53].
Input scale: [58.395,57.12,57.375].| 0.3022473 liner, ocean liner
0.1322417 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1194588 container ship, containership, container vessel
0.0795097 drilling platform, offshore rig
0.0723070 dock, dockage, docking facility| googlenet-v1-tf| Mean: [127.5, 127.5, 127.5].
Input scale: [127.5].|0.1235979 breakwater, groin, groyne, mole, bulwark, seawall, jetty
0.1017586 liner, ocean liner
0.0949444 drilling platform, offshore rig
0.0817947 container ship, containership, container vessel
0.0486889 fireboat| From 5cb32b22dcbfef631a44a664812e501d3ab42e00 Mon Sep 17 00:00:00 2001 From: igkonov Date: Fri, 10 Nov 2023 13:06:37 +0300 Subject: [PATCH 09/10] tflite docker --- docker/TensorFlowLite/Dockerfile | 30 +++++++++++++++++++++++++++ docker/TensorFlowLite/config_tf2.yml | 31 ++++++++++++++++++++++++++++ 2 files changed, 61 insertions(+) create mode 100644 docker/TensorFlowLite/Dockerfile create mode 100644 docker/TensorFlowLite/config_tf2.yml diff --git a/docker/TensorFlowLite/Dockerfile b/docker/TensorFlowLite/Dockerfile new file mode 100644 index 000000000..55b9bd8c6 --- /dev/null +++ b/docker/TensorFlowLite/Dockerfile @@ -0,0 +1,30 @@ +FROM ubuntu_for_dli + +WORKDIR /root/ +ARG TFLITE_VERSION=2.10.0 +ARG TF_VERSION=2.13.0 +ARG FRAMEWORK +RUN pip3 install --upgrade pip && \ + pip3 install opencv-python requests PyYAML docker +RUN export LD_LIBRARY_PATH=/root/miniconda3/lib:${LD_LIBRARY_PATH} + +WORKDIR /tmp/ +COPY models.lst models.lst +RUN python3 ./open_model_zoo/tools/model_tools/downloader.py --name mobilenet-v1-1.0-224-tf + +RUN pip3 install tensorflow==${TF_VERSION} +RUN pip3 install tflite==${TFLITE_VERSION} +RUN pip3 install -r ./dl-benchmark/src/model_converters/requirements.txt +RUN python3 ./open_model_zoo/tools/model_tools/converter.py --name mobilenet-v1-1.0-224-tf +RUN rm models.lst + +WORKDIR /tmp/open_model_zoo/tools/accuracy_checker +COPY config_tf2.yml config_tf2.yml + + +RUN python3 setup.py install_core +RUN accuracy_check -c config_tf2.yml -m ../../../public -s sample + +RUN rm config_tf2.yml + +WORKDIR /tmp/ \ No newline at end of file diff --git a/docker/TensorFlowLite/config_tf2.yml b/docker/TensorFlowLite/config_tf2.yml new file mode 100644 index 000000000..068bdd37f --- /dev/null +++ b/docker/TensorFlowLite/config_tf2.yml @@ -0,0 +1,31 @@ +models: + - name: mobilenet-v1-1.0-224-tf + launchers: + - framework: tf_lite + device: CPU + model: mobilenet-v1-1.0-224-tf/mobilenet_v1_1.0_224.tflite + saved_model_dir: mobilenet-v1-1.0-224-tf/ + adapter: classification + batch: 1 + + datasets: + - name: sample_dataset + data_source: sample_dataset/test + annotation_conversion: + converter: cifar + data_batch_file: cifar-10-batches-py/test_batch + convert_images: True + converted_images_dir: sample_dataset/test + num_classes: 10 + + preprocessing: + - type: resize + size: 224 + - type: bgr_to_rgb + - type: normalization + mean: (127.5, 127.5, 127.5) + std: (127.5, 127.5, 127.5) + + metrics: + - type: accuracy + top_k: 1 From 2dfd82848b0f0b3f8f1f2a9498f0cfd2993d4ab1 Mon Sep 17 00:00:00 2001 From: IgorKonovalovAleks <43146597+IgorKonovalovAleks@users.noreply.github.com> Date: Thu, 16 Nov 2023 22:23:19 +0300 Subject: [PATCH 10/10] Update Dockerfile --- docker/TensorFlowLite/Dockerfile | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docker/TensorFlowLite/Dockerfile b/docker/TensorFlowLite/Dockerfile index 55b9bd8c6..ef2555b8b 100644 --- a/docker/TensorFlowLite/Dockerfile +++ b/docker/TensorFlowLite/Dockerfile @@ -3,7 +3,6 @@ FROM ubuntu_for_dli WORKDIR /root/ ARG TFLITE_VERSION=2.10.0 ARG TF_VERSION=2.13.0 -ARG FRAMEWORK RUN pip3 install --upgrade pip && \ pip3 install opencv-python requests PyYAML docker RUN export LD_LIBRARY_PATH=/root/miniconda3/lib:${LD_LIBRARY_PATH} @@ -27,4 +26,4 @@ RUN accuracy_check -c config_tf2.yml -m ../../../public -s sample RUN rm config_tf2.yml -WORKDIR /tmp/ \ No newline at end of file +WORKDIR /tmp/