76 lines
4.0 KiB
Markdown
76 lines
4.0 KiB
Markdown
# Co-tuning for Transfer Learning <br />Supervised Contrastive Learning
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## Data preparation
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以[Kaggle数据集](https://www.kaggle.com/andrewmvd/road-sign-detection) 比赛数据为例，说明如何准备自定义数据。
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Kaggle上的 [road-sign-detection](https://www.kaggle.com/andrewmvd/road-sign-detection) 比赛数据包含877张图像，数据类别4类：crosswalk，speedlimit，stop，trafficlight。
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可从Kaggle上下载，也可以从[下载链接](https://fsdet-dataset.bj.bcebos.com/roadsign_coco.tar.gz) 下载。
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分别从原始数据集中每类选取相同样本（例如：10shots即每类都有十个训练样本）训练即可。<br />
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工业数据集使用PKU-Market-PCB，该数据集用于印刷电路板（PCB）的瑕疵检测，提供了6种常见的PCB缺陷[下载链接](https://fsdet-dataset.bj.bcebos.com/pcb.tar.gz)
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## Model Zoo
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| 骨架网络 | 网络类型 | 每张GPU图片个数 | 每类样本个数 | Box AP | 下载 | 配置文件 |
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| :------------------- | :------------- | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
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| ResNet50-vd | Faster | 1 | 10 | 60.1 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/faster_rcnn_r50_vd_fpn_1x_coco.pdparams) | [配置文件](./faster_rcnn_r50_vd_fpn_1x_coco_cotuning_roadsign.yml) |
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| PPYOLOE_crn_s | PPYOLOE | 1 | 30 | 17.8 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_s_80e_contrast_pcb.pdparams) |[配置文件](./ppyoloe_plus_crn_s_80e_contrast_pcb.yml) |
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## Compare-cotuning
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| 骨架网络 | 网络类型 | 每张GPU图片个数 |每类样本个数 | Cotuning | Box AP |
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| :------------------- | :------------- | :-----: | :-----: | :------------: | :-----: |
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| ResNet50-vd | Faster | 1 | 10 | False | 56.7 |
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| ResNet50-vd | Faster | 1 | 10 | True | 60.1 |
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## Compare-contrast
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| 骨架网络 | 网络类型 | 每张GPU图片个数 | 每类样本个数 | Contrast | Box AP |
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| :------------------- | :------------- | :-----: | :-----: | :------------: | :-----: |
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| PPYOLOE_crn_s | PPYOLOE | 1 | 30 | False | 15.4 |
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| PPYOLOE_crn_s | PPYOLOE | 1 | 30 | True | 17.8 |
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## Training & Evaluation & Inference
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### 1、Training
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```
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# -c 参数表示指定使用哪个配置文件
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# --eval 参数表示边训练边评估，训练过程中会保存验证效果最佳的checkpoint
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python tools/train.py -c configs/few-shot/faster_rcnn_r50_vd_fpn_1x_coco_cotuning_roadsign.yml --eval
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```
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### 2、Evaluation
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```
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# -c 参数表示指定使用哪个配置文件
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# -o 参数表示指定配置文件中的全局变量（覆盖配置文件中的设置）
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python tools/eval.py -c configs/few-shot/faster_rcnn_r50_vd_fpn_1x_coco_cotuning_roadsign.yml \
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-o weights=output/faster_rcnn_r50_vd_fpn_1x_coco_cotuning_roadsign/best_model
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```
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### 3、Inference
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```
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# -c 参数表示指定使用哪个配置文件
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# --infer_img 参数指定预测图像路径
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python tools/infer.py -c configs/few-shot/faster_rcnn_r50_vd_fpn_1x_coco_cotuning_roadsign.yml \
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--infer_img=demo/road554.png
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```
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## Citations
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```
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@article{you2020co,
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title={Co-tuning for transfer learning},
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author={You, Kaichao and Kou, Zhi and Long, Mingsheng and Wang, Jianmin},
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journal={Advances in Neural Information Processing Systems},
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volume={33},
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pages={17236--17246},
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year={2020}
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}
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@article{khosla2020supervised,
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title={Supervised contrastive learning},
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author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
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journal={Advances in Neural Information Processing Systems},
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volume={33},
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pages={18661--18673},
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year={2020}
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}
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``` |