add Tiny YOLOv2 model family

README.md

@@ -1,4 +1,4 @@
-# TF Keras YOLO Modelset
+# TF Keras YOLOv3/v2 Modelset
 
 [![license](https://img.shields.io/github/license/mashape/apistatus.svg)](LICENSE)
 
@@ -23,6 +23,7 @@ A common YOLOv3/v2 object detection pipeline implement with tf.keras. Including
 - [x] Tiny YOLOv3 Lite
 - [x] YOLOv2
 - [x] YOLOv2 Lite
+- [x] Tiny YOLOv2
 
 #### Loss
 - [x] Standard YOLOv3 loss
@@ -59,7 +60,7 @@ A common YOLOv3/v2 object detection pipeline implement with tf.keras. Including
 # pip install -r requirements.txt
 ```
 
-2. Download Darknet/YOLOv2/YOLOv3/YOLOv3-spp/Tiny YOLOv3 weights from [YOLO website](http://pjreddie.com/darknet/yolo/).
+2. Download Related Darknet/YOLOv2/YOLOv3 weights from [YOLO website](http://pjreddie.com/darknet/yolo/).
 3. Convert the Darknet YOLO model to a Keras model.
 4. Run YOLO detection on your image or video, default using Tiny YOLOv3 model.
 
@@ -71,12 +72,16 @@ A common YOLOv3/v2 object detection pipeline implement with tf.keras. Including
 # wget -O weights/yolov3-spp.weights https://pjreddie.com/media/files/yolov3-spp.weights
 # wget -O weights/yolov2.weights http://pjreddie.com/media/files/yolo.weights
 # wget -O weights/yolov2-voc.weights http://pjreddie.com/media/files/yolo-voc.weights
+# wget -O weights/yolov2-tiny.weights https://pjreddie.com/media/files/yolov2-tiny.weights
+# wget -O weights/yolov2-tiny-voc.weights https://pjreddie.com/media/files/yolov2-tiny-voc.weights
 
 # python tools/convert.py cfg/yolov3.cfg weights/yolov3.weights weights/yolov3.h5
 # python tools/convert.py cfg/yolov3-tiny.cfg weights/yolov3-tiny.weights weights/yolov3-tiny.h5
 # python tools/convert.py cfg/yolov3-spp.cfg weights/yolov3-spp.weights weights/yolov3-spp.h5
 # python tools/convert.py cfg/yolov2.cfg weights/yolov2.weights weights/yolov2.h5
 # python tools/convert.py cfg/yolov2-voc.cfg weights/yolov2-voc.weights weights/yolov2-voc.h5
+# python tools/convert.py cfg/yolov2-tiny.cfg weights/yolov2-tiny.weights weights/yolov2-tiny.h5
+# python tools/convert.py cfg/yolov2-tiny-voc.cfg weights/yolov2-tiny-voc.weights weights/yolov2-tiny-voc.h5
 # python tools/convert.py cfg/darknet53.cfg weights/darknet53.conv.74.weights weights/darknet53.h5
 # python tools/convert.py cfg/darknet19_448_body.cfg weights/darknet19_448.conv.23.weights weights/darknet19.h5
 

cfg/yolov2-tiny-voc.cfg

@@ -0,0 +1,138 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+max_batches = 40200
+policy=steps
+steps=-1,100,20000,30000
+scales=.1,10,.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=125
+activation=linear
+
+[region]
+anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
+bias_match=1
+classes=20
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1

cfg/yolov2-tiny.cfg

@@ -0,0 +1,139 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=425
+activation=linear
+
+[region]
+anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
+bias_match=1
+classes=80
+coords=4
+num=5
+softmax=1
+jitter=.2
+rescore=0
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1

configs/yolo2-tiny-voc_anchors.txt

@@ -0,0 +1 @@
+1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52

configs/yolo2-tiny_anchors.txt

@@ -0,0 +1 @@
+0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828

yolo2/model.py

@@ -9,9 +9,9 @@
 from tensorflow.keras.models import Model
 from tensorflow.keras.optimizers import Adam
 
-from yolo2.models.yolo2_darknet import yolo2_body
-from yolo2.models.yolo2_mobilenet import yolo2_mobilenet_body, yolo2lite_mobilenet_body
-from yolo2.models.yolo2_mobilenetv2 import yolo2_mobilenetv2_body, yolo2lite_mobilenetv2_body
+from yolo2.models.yolo2_darknet import yolo2_body, tiny_yolo2_body
+from yolo2.models.yolo2_mobilenet import yolo2_mobilenet_body, yolo2lite_mobilenet_body, tiny_yolo2_mobilenet_body, tiny_yolo2lite_mobilenet_body
+from yolo2.models.yolo2_mobilenetv2 import yolo2_mobilenetv2_body, yolo2lite_mobilenetv2_body, tiny_yolo2_mobilenetv2_body, tiny_yolo2lite_mobilenetv2_body
 from yolo2.models.yolo2_xception import yolo2_xception_body, yolo2lite_xception_body
 from yolo2.loss import yolo2_loss
 from yolo2.postprocess import batched_yolo2_postprocess
@@ -32,6 +32,12 @@
     'yolo2_mobilenetv2_lite': [yolo2lite_mobilenetv2_body, 155, None],
     'yolo2_xception': [yolo2_xception_body, 132, None],
     'yolo2_xception_lite': [yolo2lite_xception_body, 132, None],
+
+    'tiny_yolo2_darknet': [tiny_yolo2_body, 0, None],
+    'tiny_yolo2_mobilenet': [tiny_yolo2_mobilenet_body, 87, None],
+    'tiny_yolo2_mobilenet_lite': [tiny_yolo2lite_mobilenet_body, 87, None],
+    'tiny_yolo2_mobilenetv2': [tiny_yolo2_mobilenetv2_body, 155, None],
+    'tiny_yolo2_mobilenetv2_lite': [tiny_yolo2lite_mobilenetv2_body, 155, None],
 }