Webly Supervised Image Classification with Self-Contained Confidence.
Jingkang Yang, Litong Feng, Weirong Chen, Xiaopeng Yan, Huabin Zheng, Ping Luo, Wayne Zhang
SenseTime Research, SenseTime.
We introduce Self-Contained Confidence (SCC) by adapting model uncertainty for WSL setting, and use it to sample-wisely balance the self-label supervised loss and the webly supervised loss. Therefore, a simple yet effective WSL framework is proposed.
The paper has been accepted by ECCV 2020. For more details, please refer to our paper.
To install requirements:
pip install -r requirements.txt
Put this git repository in a root directory. And in the root directory, run
mkdir checkpoint
mkdir data
mkdir imglists
The experiments used Food101 and WebVision datasets.
You can download the image list files in the imglists folder for reading datasets here:
To run the baseline in the paper, run this command:
sh ./scripts/WSL/food101n/base.sh
To run the SCC in the paper, run this command:
sh ./scripts/WSL/food101n/offline_scc.sh
You can download pretrained models here:
Our model achieves the following performance on Food101N dataset:
| Model name | Top 1 Accuracy | Top 5 Accuracy |
|---|---|---|
| baseline | 83.66% | 94.97% |
| offline scc | 86.44% | 96.77% |
Our model achieves the following performance on Google500 dataset:
| Model name | Top 1 Accuracy | Top 5 Accuracy |
|---|---|---|
| baseline | 68.31% | 82.12% |
| offline scc | 69.16% | 82.66% |
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If you find our code useful，please cite papers:
Webly Supervised Image Classification with Self-Contained Confidence
Learning Image Classifier from Only Web Labels and Metadata: Automatic Label Correction through Graph (ACM-MM Oral Presentation), 2020