update

config/yolov5_af_config.py

@@ -45,7 +45,7 @@ class Yolov5AFBaseConfig(object):
         self.val_conf_thresh = 0.001
         self.val_nms_thresh  = 0.7
         self.test_topk = 100
-        self.test_conf_thresh = 0.2
+        self.test_conf_thresh = 0.3
         self.test_nms_thresh  = 0.5
 
         # ---------------- Assignment config ----------------

config/yolov5_config.py

@@ -48,7 +48,7 @@ class Yolov5BaseConfig(object):
         self.val_conf_thresh = 0.001
         self.val_nms_thresh  = 0.7
         self.test_topk = 100
-        self.test_conf_thresh = 0.2
+        self.test_conf_thresh = 0.3
         self.test_nms_thresh  = 0.5
 
         # ---------------- Assignment config ----------------

models/yolov3/README.md

@@ -0,0 +1,60 @@
+# Redesigned YOLOv3:
+
+- VOC
+
+|   Model  | Batch | Scale | AP<sup>val<br>0.5 | Weight |  Logs  |
+|----------|-------|-------|-------------------|--------|--------|
+| YOLOv3-S | 1xb16 |  640  |       75.5        | [ckpt](https://github.com/yjh0410/YOLO-Tutorial-v3/releases/download/yolo_tutorial_ckpt/yolov3_s_voc.pth) | [log](https://github.com/yjh0410/YOLO-Tutorial-v3/releases/download/yolo_tutorial_ckpt/YOLOv3-S-VOC.txt) |
+
+- COCO
+
+|   Model  | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |  Logs  |
+|----------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|--------|
+| YOLOv3-S | 1xb16 |  640  |                    |               |   25.2            |   7.3             |  |  |
+
+- For training, we train redesigned YOLOv3 with 150 epochs on COCO. We also gradient accumulate.
+- For data augmentation, we only use the large scale jitter (LSJ), no Mosaic or Mixup augmentation.
+- For optimizer, we use SGD with momentum 0.937, weight decay 0.0005 and base lr 0.01.
+- For learning rate scheduler, we use linear decay scheduler.
+
+
+## Train YOLOv3
+### Single GPU
+Taking training YOLOv3 on COCO as the example,
+```Shell
+python train.py --cuda -d coco --root path/to/coco -m yolov3 -bs 16 -size 640 --wp_epoch 3 --max_epoch 150 --eval_epoch 10 --no_aug_epoch 10 --ema --fp16 --multi_scale 
+```
+
+### Multi GPU
+Taking training YOLOv3 on COCO as the example,
+```Shell
+python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m yolov3 -bs 128 -size 640 --wp_epoch 3 --max_epoch 150  --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --sybn --multi_scale --save_folder weights/ 
+```
+
+## Test YOLOv3
+Taking testing YOLOv3 on COCO-val as the example,
+```Shell
+python test.py --cuda -d coco --root path/to/coco -m yolov3 --weight path/to/yolov3.pth -size 640 -vt 0.3 --show 
+```
+
+## Evaluate YOLOv3
+Taking evaluating YOLOv3 on COCO-val as the example,
+```Shell
+python eval.py --cuda -d coco-val --root path/to/coco -m yolov3 --weight path/to/yolov3.pth 
+```
+
+## Demo
+### Detect with Image
+```Shell
+python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov3 --weight path/to/weight -size 640 -vt 0.3 --show
+```
+
+### Detect with Video
+```Shell
+python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov3 --weight path/to/weight -size 640 -vt 0.3 --show --gif
+```
+
+### Detect with Camera
+```Shell
+python demo.py --mode camera --cuda -m yolov3 --weight path/to/weight -size 640 -vt 0.3 --show --gif
+```

models/yolov5/README.md

@@ -0,0 +1,60 @@
+# Redesigned YOLOv5:
+
+- VOC
+
+|   Model  | Batch | Scale | AP<sup>val<br>0.5 | Weight |  Logs  |
+|----------|-------|-------|-------------------|--------|--------|
+| YOLOv5-S | 1xb16 |  640  |       79.0        | [ckpt](https://github.com/yjh0410/YOLO-Tutorial-v5/releases/download/yolo_tutorial_ckpt/yolov5_s_voc.pth) | [log](https://github.com/yjh0410/YOLO-Tutorial-v5/releases/download/yolo_tutorial_ckpt/YOLOv5-S-VOC.txt) |
+
+- COCO
+
+|  Model   | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |  Logs  |
+|----------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|--------|
+| YOLOv5-S | 1xb16 |  640  |                    |               |   27.3            |   9.0             |  |  |
+
+- For training, we train redesigned YOLOv5 with 150 epochs on COCO. We also gradient accumulate.
+- For data augmentation, we only use the large scale jitter (LSJ), no Mosaic or Mixup augmentation.
+- For optimizer, we use SGD with momentum 0.937, weight decay 0.0005 and base lr 0.01.
+- For learning rate scheduler, we use linear decay scheduler.
+
+
+## Train YOLOv5
+### Single GPU
+Taking training YOLOv5 on COCO as the example,
+```Shell
+python train.py --cuda -d coco --root path/to/coco -m yolov5 -bs 16 -size 640 --wp_epoch 3 --max_epoch 150 --eval_epoch 10 --no_aug_epoch 10 --ema --fp16 --multi_scale 
+```
+
+### Multi GPU
+Taking training YOLOv5 on COCO as the example,
+```Shell
+python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m yolov5 -bs 128 -size 640 --wp_epoch 3 --max_epoch 150  --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --sybn --multi_scale --save_folder weights/ 
+```
+
+## Test YOLOv5
+Taking testing YOLOv5 on COCO-val as the example,
+```Shell
+python test.py --cuda -d coco --root path/to/coco -m yolov5 --weight path/to/yolov5.pth -size 640 -vt 0.3 --show 
+```
+
+## Evaluate YOLOv5
+Taking evaluating YOLOv5 on COCO-val as the example,
+```Shell
+python eval.py --cuda -d coco-val --root path/to/coco -m yolov5 --weight path/to/yolov5.pth 
+```
+
+## Demo
+### Detect with Image
+```Shell
+python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov5 --weight path/to/weight -size 640 -vt 0.3 --show
+```
+
+### Detect with Video
+```Shell
+python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov5 --weight path/to/weight -size 640 -vt 0.3 --show --gif
+```
+
+### Detect with Camera
+```Shell
+python demo.py --mode camera --cuda -m yolov5 --weight path/to/weight -size 640 -vt 0.3 --show --gif
+```