keep training YOLOv3-Tiny from epoch 171

README.md

@@ -145,7 +145,7 @@ I have provided a bash file `train_ddp.sh` that enables DDP training. I hope som
 | Model         |   Backbone         | Scale | Epoch | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
 |---------------|--------------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
 | YOLOv7-T      | ELANNet-Tiny       |  640  |  300  |         38.0           |       56.8        |   22.6            |   7.9              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_tiny_coco.pth) |
-| YOLOv7-L      | ELANNet-Large      |  640  |  300  |                        |                   |   144.6           |   44.0             |  |
+| YOLOv7-L      | ELANNet-Large      |  640  |  300  |         48.0           |       67.5        |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_coco.pth) |
 
 *While YOLOv7 incorporates several technical details, such as anchor box, SimOTA, AuxiliaryHead, and RepConv, I found it too challenging to fully reproduce. Instead, I created a simpler version of YOLOv7 using an anchor-free structure and SimOTA. As a result, my reproduction had poor performance due to the absence of the other technical details. However, since it was only intended as a tutorial, I am not too concerned about this gap.*
 

train.sh

@@ -12,7 +12,7 @@ python train.py \
         --ema \
         --fp16 \
         --multi_scale \
-        --resume weights/coco/yolov3_t/yolov3_t_epoch_141_22.32.pth \
+        --resume weights/coco/yolov3_t/yolov3_t_epoch_171_23.04.pth \
         # --pretrained weights/coco/yolo_free_medium/yolo_free_medium_39.46.pth \
         # --eval_first