modify the link to pretrained weight

README_CN.md

@@ -1,20 +1,14 @@
-# PyTorch_YOLO_Tutorial
-YOLO 教程
-
-# 简介
-这是一个讲解YOLO的入门教程的代码。在这个项目中，我们继承了YOLOv1~v4、YOLOX以及YOLOv7的中心思想，并在此基础上做了适当的修改来实现了结构较为简洁的YOLO检测器。我们希望通过初学者可以通过学习流行的YOLO检测器顺利入门目标检测领域。
-
-**书籍链接**：与本项目代码配套的技术书籍正在被校阅中，请耐心等待。
+# 实时通用目标检测
 
 ## 配置运行环境
 - 首先，我们建议使用Anaconda来创建一个conda的虚拟环境
 ```Shell
-conda create -n yolo python=3.6
+conda create -n rtcdet python=3.6
 ```
 
 - 然后, 请激活已创建的虚拟环境
 ```Shell
-conda activate yolo
+conda activate rtcdet
 ```
 
 - 接着，配置环境:

models/detectors/yolov1/README.md

@@ -2,7 +2,7 @@
 
 | Model  |  Backbone  | 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 |
 |--------|------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOv1 | ResNet-18  | 1xb16 |  640  |        27.9            |       47.5        |   37.8            |   21.3             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_coco.pth) |
+| YOLOv1 | ResNet-18  | 1xb16 |  640  |        27.9            |       47.5        |   37.8            |   21.3             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov1_coco.pth) |
 
 - For training, we train redesigned YOLOv1 with 150 epochs on COCO.
 - For data augmentation, we only use the large scale jitter (LSJ), no Mosaic or Mixup augmentation.

models/detectors/yolov2/README.md

@@ -2,7 +2,7 @@
 
 | Model  |  Backbone  | 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 |
 |--------|------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOv2 | DarkNet-19 | 1xb16 |  640  |        32.7            |       50.9        |   53.9            |   30.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_coco.pth) |
+| YOLOv2 | DarkNet-19 | 1xb16 |  640  |        32.7            |       50.9        |   53.9            |   30.9             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov2_coco.pth) |
 
 - For training, we train redesigned YOLOv2 with 150 epochs on COCO.
 - For data augmentation, we only use the large scale jitter (LSJ), no Mosaic or Mixup augmentation.

models/detectors/yolov3/README.md

@@ -2,8 +2,8 @@
 
 |    Model    |   Backbone   | 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 |
 |-------------|--------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOv3-Tiny | DarkNet-Tiny | 1xb16 |  640  |        25.4            |       43.4        |   7.0             |   2.3              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_t_coco.pth) |
-| YOLOv3      | DarkNet-53   | 1xb16 |  640  |        42.9            |       63.5        |   167.4           |   54.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_coco.pth) |
+| YOLOv3-Tiny | DarkNet-Tiny | 1xb16 |  640  |        25.4            |       43.4        |   7.0             |   2.3              | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov3_t_coco.pth) |
+| YOLOv3      | DarkNet-53   | 1xb16 |  640  |        42.9            |       63.5        |   167.4           |   54.9             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov3_coco.pth) |
 
 - For training, we train YOLOv3 and YOLOv3-Tiny with 250 epochs on COCO.
 - For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of [YOLOv5](https://github.com/ultralytics/yolov5).

models/detectors/yolov4/README.md

@@ -2,8 +2,8 @@
 
 |    Model    |     Backbone    | 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 |
 |-------------|-----------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOv4-Tiny | CSPDarkNet-Tiny | 1xb16 |  640  |        31.0            |       49.1        |   8.1             |   2.9              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_t_coco.pth) |
-| YOLOv4      | CSPDarkNet-53   | 1xb16 |  640  |        46.6            |       65.8        |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_coco.pth) |
+| YOLOv4-Tiny | CSPDarkNet-Tiny | 1xb16 |  640  |        31.0            |       49.1        |   8.1             |   2.9              | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov4_t_coco.pth) |
+| YOLOv4      | CSPDarkNet-53   | 1xb16 |  640  |        46.6            |       65.8        |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov4_coco.pth) |
 
 - For training, we train YOLOv4 and YOLOv4-Tiny with 250 epochs on COCO.
 - For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of [YOLOv5](https://github.com/ultralytics/yolov5).

models/detectors/yolov5/README.md

@@ -2,10 +2,10 @@
 
 |   Model   |   Backbone   | 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 |
 |-----------|--------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOv5-N  | CSPDarkNet-N | 1xb16 |  640  |         29.8           |       47.1        |   7.7             |   2.4              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_n_coco.pth) |
-| YOLOv5-S  | CSPDarkNet-S | 1xb16 |  640  |         37.8           |       56.5        |   27.1            |   9.0              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_s_coco.pth) |
-| YOLOv5-M  | CSPDarkNet-M | 1xb16 |  640  |         43.5           |       62.5        |   74.3            |   25.4             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_m_coco.pth) |
-| YOLOv5-L  | CSPDarkNet-L | 1xb16 |  640  |         46.7           |       65.5        |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_l_coco.pth) |
+| YOLOv5-N  | CSPDarkNet-N | 1xb16 |  640  |         29.8           |       47.1        |   7.7             |   2.4              | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov5_n_coco.pth) |
+| YOLOv5-S  | CSPDarkNet-S | 1xb16 |  640  |         37.8           |       56.5        |   27.1            |   9.0              | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov5_s_coco.pth) |
+| YOLOv5-M  | CSPDarkNet-M | 1xb16 |  640  |         43.5           |       62.5        |   74.3            |   25.4             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov5_m_coco.pth) |
+| YOLOv5-L  | CSPDarkNet-L | 1xb16 |  640  |         46.7           |       65.5        |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov5_l_coco.pth) |
 
 - For training, we train YOLOv5 series with 300 epochs on COCO.
 - For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of [YOLOv5](https://github.com/ultralytics/yolov5).

models/detectors/yolov7/README.md

@@ -2,8 +2,8 @@
 
 |    Model    |   Backbone    | 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 |
 |-------------|---------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOv7-Tiny | ELANNet-Tiny  | 8xb16 |  640  |         39.5           |       58.5        |   22.6            |   7.9              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_tiny_coco.pth) |
-| YOLOv7      | ELANNet-Large | 8xb16 |  640  |         49.5           |       68.8        |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_coco.pth) |
+| YOLOv7-Tiny | ELANNet-Tiny  | 8xb16 |  640  |         39.5           |       58.5        |   22.6            |   7.9              | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov7_tiny_coco.pth) |
+| YOLOv7      | ELANNet-Large | 8xb16 |  640  |         49.5           |       68.8        |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov7_coco.pth) |
 | YOLOv7-X    | ELANNet-Huge  |       |  640  |                        |                   |                   |                    |  |
 
 - For training, we train `YOLOv7` and `YOLOv7-Tiny` with 300 epochs on 8 GPUs.

models/detectors/yolox/README.md

@@ -2,9 +2,9 @@
 
 |   Model |   Backbone   | 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 |
 |---------|--------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
-| YOLOX-S | CSPDarkNet-S | 8xb8  |  640  |         40.1           |       60.3        |   26.8            |   8.9              | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_s_coco.pth) |
-| YOLOX-M | CSPDarkNet-M | 8xb8  |  640  |         46.2           |       66.0        |   74.3            |   25.4             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_m_coco.pth) |
-| YOLOX-L | CSPDarkNet-L | 8xb8  |  640  |         48.7           |       68.0        |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_l_coco.pth) |
+| YOLOX-S | CSPDarkNet-S | 8xb8  |  640  |         40.1           |       60.3        |   26.8            |   8.9              | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolox_s_coco.pth) |
+| YOLOX-M | CSPDarkNet-M | 8xb8  |  640  |         46.2           |       66.0        |   74.3            |   25.4             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolox_m_coco.pth) |
+| YOLOX-L | CSPDarkNet-L | 8xb8  |  640  |         48.7           |       68.0        |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolox_l_coco.pth) |
 | YOLOX-X | CSPDarkNet-X | 8xb8  |  640  |                        |                   |                   |                    |  |
 
 - For training, we train YOLOX series with 300 epochs on COCO.