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PaddlePaddle Denglin Model Zoo

🤝 百度飞桨 x 登临科技AI模型库

  • 兼容性适配：目前登临科技与百度飞桨深度学习框架已完成三级兼容性适配认证，支持当下主流模型应用场景，覆盖了计算机视觉、智能语音、自然语言处理、推荐、图神经网络和强化学习等领域，支持当下主流模型数量100+;
  • 一键启动：通过兼容飞桨推理接口，用户通过指定enable_dlnne()接口一键启动模型，并部署在登临GPU上执行;
  • 性能评估：开启enable_profile()接口即可评估模型性能;
  • 支持拓展：用户可自行准备飞桨预训练inference模型，通过登临GPU实现加速推理;
  • 其他特性：有关enable_dlnne()接口的是详细使用方法可参考Paddle-dlNNE;

📦 模型信息

PaddleClas

Models Evaluate Datasets Input shape Acc(paddle) Acc(Denglin GPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
AlexNet ImageNet1k 1x3x224x224 0.567 0.56644 8.388 inference_model
DenseNet121 ImageNet1k 1x3x224x224 0.7566 0.75666 9.889 inference_model
DLA102 ImageNet1k 1x3x224x224 0.7893 0.78926 14.285 inference_model
DLA34 ImageNet1k 1x3x224x224 0.7603 0.76028 5.676 inference_model
DLA46_c ImageNet1k 1x3x224x224 0.6321 0.63210 2.488 inference_model
DLA60 ImageNet1k 1x3x224x224 0.7610 0.76102 8.660 inference_model
DLA60x_c ImageNet1k 1x3x224x224 0.6645 0.66448 2.907 inference_model
DPN68 ImageNet1k 1x3x224x224 0.7678 0.76780 7.785 inference_model
DPN92 ImageNet1k 1x3x224x224 0.7985 0.79852 23.162 inference_model
ESNet_x0_25 ImageNet1k 1x3x224x224 0.6248 0.62462 9.464 inference_model
ESNet_x0_5 ImageNet1k 1x3x224x224 0.6882 0.68820 9.857 inference_model
GoogleNet ImageNet1k 1x3x224x224 0.7070 0.70690 4.678 inference_model
HarDNet39_ds ImageNet1k 1x3x224x224 0.7133 0.71332 2.968 inference_model
HarDNet68_ds ImageNet1k 1x3x224x224 0.7362 0.73618 4.298 inference_model
HRNet_W18_C ImageNet1k 1x3x224x224 0.7692 0.76890 18.502 inference_model
MixNet_S ImageNet1k 1x3x224x224 0.7628 0.76282 10.055 inference_model
MobileNetV1 ImageNet1k 1x3x224x224 0.7099 0.71000 3.750 inference_model
MobileNetV2 ImageNet1k 1x3x224x224 0.7215 0.72156 3.251 inference_model
MobileNetV3_small_x0_35_ssld ImageNet1k 1x3x224x224 0.5555 0.55606 2.271 inference_model
MobileNetV3_small_x0_5 ImageNet1k 1x3x224x224 0.5921 0.59192 2.484 inference_model
MobileNetV3_small_x0_75 ImageNet1k 1x3x224x224 0.6602 0.66050 3.150 inference_model
MobileNetV3_small_x1_25 ImageNet1k 1x3x224x224 0.7067 0.70654 4.297 inference_model
PPLCNet_x0_25 ImageNet1k 1x3x224x224 0.5186 0.51812 2.287 inference_model
PPLCNet_x0_35 ImageNet1k 1x3x224x224 0.5809 0.58088 2.747 inference_model
PPLCNet_x0_5 ImageNet1k 1x3x224x224 0.6314 0.63172 2.921 inference_model
PPLCNet_x1_0 ImageNet1k 1x3x224x224 0.7132 0.71312 4.591 inference_model
RedNet26 ImageNet1k 1x3x224x224 0.7595 0.75950 219.600 inference_model
RedNet38 ImageNet1k 1x3x224x224 0.7747 0.77470 333.226 inference_model
Res2Net50_14w_8s ImageNet1k 1x3x224x224 0.7946 0.79462 11.200 inference_model
ResNet101_vd ImageNet1k 1x3x224x224 0.8017 0.80178 12.615 inference_model
ResNet18 ImageNet1k 1x3x224x224 0.7098 0.70988 3.411 inference_model
ResNet50 ImageNet1k 1x3x224x224 0.7650 0.76502 7.597 inference_model
ResNeXt50_32x4d ImageNet1k 1x3x224x224 0.7775 0.77754 9.849 inference_model
ReXNet_1_0 ImageNet1k 1x3x224x224 0.7746 0.77452 23.933 inference_model
ReXNet_1_3 ImageNet1k 1x3x224x224 0.7913 0.79134 28.345 inference_model
ReXNet_1_5 ImageNet1k 1x3x224x224 0.8006 0.80072 30.896 inference_model
ReXNet_2_0 ImageNet1k 1x3x224x224 0.8122 0.81242 39.879 inference_model
ReXNet_3_0 ImageNet1k 1x3x224x224 0.8209 0.82086 60.846 inference_model
SE_ResNet18_Vd ImageNet1k 1x3x224x224 0.7333 0.73332 4.402 inference_model
SE_ResNet34_vd ImageNet1k 1x3x224x224 0.7651 0.76518 7.116 inference_model
SE_ResNet50_vd ImageNet1k 1x3x224x224 0.7952 0.79524 15.831 inference_model
ShuffleNetV2_x0_25 ImageNet1k 1x3x224x224 0.4990 0.49904 12.256 inference_model
ShuffleNetV2_x1_5 ImageNet1k 1x3x224x224 0.7163 0.71636 14.337 inference_model
SqueezeNet1_1 ImageNet1k 1x3x224x224 0.601 0.60076 2.295 inference_model
VGG11 ImageNet1k 1x3x224x224 0.693 0.69294 21.421 inference_model
VGG13 ImageNet1k 1x3x224x224 0.700 0.69994 24.873 inference_model
VGG19 ImageNet1k 1x3x224x224 0.726 0.72556 31.127 inference_model

PaddleOCR

Models Evaluate Datasets Input shape Hmean(paddle) Hmean(Denglin GPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
det_mv3_db_v2.0 ICDAR2015 1x3x736x1280 0.7512 0.75092 96.365 inference_model
det_r50_vd_db_v2.0 ICDAR2015 1x3x736x1280 0.8238 0.82368 318.926 inference_model
det_mv3_east_v2.0 ICDAR2015 1x3x704x1280 0.7865 0.78680 74.671 inference_model
det_r50_vd_east_v2.0 ICDAR2015 1x3x704x1280 0.8488 0.84903 408.758 inference_model
det_r50_vd_sast_icdar15_v2.0 ICDAR2015 1x3x896x1536 0.8742 0.87415 1772.236 inference_model
det_mv3_pse_v2.0 ICDAR2015 1x3x736x1312 0.7589 0.75894 304.274 inference_model
det_r50_vd_pse_v2.0 ICDAR2015 1x3x736x1312 0.8255 0.82538 674.206 inference_model
rec_svtr_tiny_none_ctc_en IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE 1x3x64x256 0.9013(Avg_10,acc) 0.90105 (acc) 6.564 inference_model

PaddleDetection

Models Evaluate Datasets Input shape mAP(paddle) mAP(Denglin GPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
picodet_lcnet_1_5x_416_coco coco 1x3x416x416 0.363 0.363 133.012 inference_model
picodet_s_320_coco coco 1x3x320x320 0.271 0.271 66.497 inference_model
ppyolo_mbv3_large_coco coco 1x3x320x320 0.232 0.240 27.512 inference_model
ppyolo_r50vd_dcn_1x_coco coco 1x3x608x608 0.448 0.447 444.563 inference_model
ppyolo_tiny_650e_coco coco 1x3x320x320 0.206 0.207 29.661 inference_model
ppyoloe_crn_s_300e_coco coco 1x3x640x640 0.430 0.430 115.896 inference_model
ppyolov2_r50vd_dcn_365e_ coco 1x3x640x640 0.491 0.491 630.109 inference_model
ttfnet_darknet53_1x_coco coco 1x3x512x512 0.335 0.336 413.021 inference_model
yolov3_darknet53_270e_coco coco 1x3x608x608 0.391 0.391 279.647 inference_model
yolov3_mobilenet_v1_270e_coco coco 1x3x608x608 0.294 0.294 136.460 inference_model
yolox_s_300e_coco coco 1x3x640x640 0.404 0.404 142.276 inference_model

PaddleSeg

Models Evaluate Datasets Input shape mIoU(paddle) mIoU(Denglin GPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
BiSeNetV1 Cityscapes 1x3x1024x2048 0.7519 0.75191 23283.000 inference_model
BiSeNetv2 Cityscapes 1x3x1024x2048 0.7319 0.73169 594.874 inference_model
CCNet Cityscapes 1x3x1025x2049 0.8095 0.80951 6435.860 inference_model
DDRNet_23(DDRNet） Cityscapes 1x3x1024x2048 0.7985 0.79847 729.794 inference_model
DeepLabv3p_resnet50_cityscapes Cityscapes 1x3x1024x2048 0.8036 0.8036 3712.280 inference_model
ENet Cityscapes 1x3x1024x2048 0.6742 0.67420 801.838 inference_model
FCN_HRNet_W18 飞桨内部人像数据集 1x3x1024x2048 0.787 0.78969 1580.298 inference_model
GloRe Cityscapes 1x3x1024x2048 0.7826 0.78256 31732.400 inference_model
HRNetW48Contrast Cityscapes 1x3x1024x2048 0.8230 0.82398 3544.080 inference_model
OCRNet_HRNetW18 Cityscapes 1x3x1024x2048 0.8067 0.80702 3801.400 inference_model
PFPNNet Cityscapes 1x3x1024x2048 0.7907 0.79072 28974.200 inference_model
STDC_STDC1 Cityscapes 1x3x1024x2048 0.7474 0.74739 904.822 inference_model
UPERNet ADE20K 1x3x1024x2048 0.7958 0.79581 8477.040 inference_model

PaddleNLP

Models Evaluate Datasets Sequence Length Acc(paddle) Acc(Denglin GPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
BERT-Base SST-2 128 0.92660 0.92661 20.455 inference_model
Bi-LSTM ChnSentiCorp 599 0.8983 0.89833 25.231 inference_model
ConvBert SST-2 128 0.9139 0.91399 102.281 inference_model
ELECTRA SST-2 128 0.9185 0.91857 4.601 inference_model
LayoutLM FUNSD 512 F1: 0.7913 F1: 0.79116 172.491 inference_model
MiniLMv2 AFQMC 128 0.7138 0.71362 9.536 inference_model
seq2seq IWSLT15 en-vi 128 BLEU: 0.2433 BLEU: 0.24340 782.965 inference_model
TextCNN ChnSentiCorp 599 0.9107 0.91000 1.273 inference_model
TinyBert SST-2 128 0.9300 0.93005 20.583 inference_model

PaddleRec

Models Evaluate Datasets Metrics(paddle) Metrics(Denglin GPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
DSSM BQ 0.93(正序率) 0.92875(正序率) 2.805 inference_model
match-pyramid Letor07 0.39(map) 0.39296map) 0.895 inference_model
NCF movielens 0.58(HR@10) 、0.33(NDCG@10) 0.58543(HR@10) 、 0.33538(NDCG@10) 0.699 inference_model
DLRM criteo Auc:0.79 + 0.80120 6.016 inference_model
DeepFM Criteo Auc:0.78 0.794357 1.357 inference_model

PARL

Models Evaluate Datasets Metrics Reward(CPU) Reward(Denglin GPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
DQN_variant Atari games Reward 3.66667 3.66667 7.171 inference_model
PPO Atari games Reward -21.0 -21.0 1.587 inference_model
DQN CartPole-v0 Reward 19.0 19.0 0.350 inference_model
MADDPG gym Reward -75.19758 -75.19758 0.768 inference_model

PGL

Models Evaluate Datasets Acc(paddle) Acc(Denglin GPU) Acc(CPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
gin MUTAG -- 0.78947 0.78947 10.529 inference_model
GraphSage reddit -- 0.74706 0.74706 581.025 inference_model
gat cora 0.83 0.83333 -- 103.456 inference_model
gcn CORA 0.81 0.81000 -- 109.458 inference_model

PaddleSpeech

Models Evaluate Datasets Metrics Acc(paddle) Acc(Denglin GPU) Acc(CPU) Latency(ms)(Denglin GPU,BS=1) Inference Model
hifigan AISHELL-3 mel_loss 0.1068 -- 0.10699 104.712 inference_model
Tacotron2 CSMSC eval/loss -- 1.928438 1.928438 -- inference_model
Speedyspeech CSMSC eval/loss -- 0.879209 0.879209 146.011 inference_model

🎈 推理预测

以图像分类为例简要介绍模型使用方法，其他模型场景详细用法请参考飞桨官方模型库：

1.模型准备： 通过链接下载登临飞桨ImageNet1K图像分类模型，例如 MobileNetV3.pdmodel 、 MobileNetV3.pdiparams
2.数据准备： 输入图像应符合NCHW Format , Shape 为 [1,3,224,224]
3.执行推理：
python3 tools/deploy/predict.py                             \
    --model_file    ${MODEL_PATH}/MobileNetV3.pdmodel       \
    --params_file   ${MODEL_PATH}/MobileNetV3.pdiparams     \
    --input_data    ${INPUT_DATA}
4.获取最终推理结果，如图像类别、Bouding Box可视化、OCR检测结果等，可参考飞桨模型库相关代码

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