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https://github.com/anonymoussss/YOLOX-SwinTransformer
YOLOX with SwinTransformer backbone.
https://github.com/anonymoussss/YOLOX-SwinTransformer
Last synced: 3 months ago
JSON representation
YOLOX with SwinTransformer backbone.
- Host: GitHub
- URL: https://github.com/anonymoussss/YOLOX-SwinTransformer
- Owner: anonymoussss
- License: apache-2.0
- Created: 2022-03-04T03:45:20.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-03-04T03:50:14.000Z (over 2 years ago)
- Last Synced: 2024-06-16T15:44:02.791Z (5 months ago)
- Language: Python
- Size: 367 KB
- Stars: 25
- Watchers: 2
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YOLOX with Swin-Transformer backbone
## YOLOX Version
**[0.1.1]** , Aug, 2021
## Introduction
> In short, the content of this repository is yolox with Swin-Transformer as the backbone.
> 简而言之，这个仓库的内容是以swin-transformer为backbone的yolox。
YOLOX is an anchor-free version of YOLO, with a simpler design but better performance. I rewrote the version with Swin-Transformer as backbone following Swin-Transformer-Object-Detection(https://github.com/SwinTransformer/Swin-Transformer-Object-Detection).
**First of all, due to limited time, I did not experiment on the COCO dataset. All results are built on my private dataset, which cannot be shared.** The composition of my dataset is not complicated, with only one class of targets, \~ 1w training images and about \~ 1.5k test images.
I used the official Swin's pretrained model (https://github.com/microsoft/Swin-Transformer) and the detection version Swin's pretrained model (https://github.com/SwinTransformer/Swin-Transformer-Object-Detection) for experiments. **My experimental results show that using COCO pre-training model works better than using ImageNet pre-training model**. The pretrained model type can be set directly in the configuration file.
For YOLOX with Swin backbone, I set the depth and width factor of PANet neck part with fixed 1.00, for example, ` self.depth = 1.00 self.width = 1.00` in config file. I simply replaced the backbone part with Swin-T/S/B.
## Usage
For example,
```python
python tools/train.py -f exps/default/yolox_swinB_coco_.py -d 8 -b 64 --fp16 --cache
```
## Results (My private dataset, not COCO !)
#### Standard Models.
|Model |size |mAPtest
0.5:0.95 |
| ------ | :---: | :---: |
|YOLOX-m |640 |77.04 |
|YOLOX-l |640 |72.51 |
|YOLOX-x |640 |**78.07** |
#### ImageNet Pretrained Models.
`To use ImageNet pre-training, please download the pre-trained model from the [website](https://github.com/microsoft/Swin-Transformer) and place it in the ./pretrained directory.`
|Backbone |size |mAPtest
0.5:0.95 | pretrained model|
| ------ | :---: | :---: | :---: |
|swin-base |320 |72.85 |swin_base_patch4_window7_224_22k.pth |
#### COCO Pretrained Models.
`To use COCO pre-training, please download the pre-trained model from the [website](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection) and place it in the ./pretrained directory.`
|Backbone |size |mAPtest
0.5:0.95 | pretrained model|
| ------ | :---: | :---: | :---: |
|swin-small |320 |73.72 | mask_rcnn_swin_tiny_patch4_window7_3x|
|swin-base |320 |75.06 | cascade_mask_rcnn_swin_base_patch4_window7_3x|
|swin-tiny |640 |76.10 | mask_rcnn_swin_tiny_patch4_window7_3x|
|swin-small |640 |76.81 | mask_rcnn_swin_tiny_patch4_window7_3x|
|swin-base |640 |**77.25** | cascade_mask_rcnn_swin_base_patch4_window7_3x|
## Some Records
- the curve of yolox_m with size 640
- the curve of yolox with swin-S backbone & size 320
- the curve of yolox with swin-S backbone & size 320