https://github.com/salesforce/MoPro
MoPro: Webly Supervised Learning
https://github.com/salesforce/MoPro
noisy-labels representation-learning weakly-supervised-learning webly-supervised-learning
Last synced: about 1 year ago
JSON representation
MoPro: Webly Supervised Learning
- Host: GitHub
- URL: https://github.com/salesforce/MoPro
- Owner: salesforce
- Created: 2020-09-08T19:57:23.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-03-03T01:34:34.000Z (over 5 years ago)
- Last Synced: 2025-04-16T03:08:37.771Z (about 1 year ago)
- Topics: noisy-labels, representation-learning, weakly-supervised-learning, webly-supervised-learning
- Language: Python
- Homepage:
- Size: 602 KB
- Stars: 87
- Watchers: 8
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## MoPro: Webly Supervised Learning with Momentum Prototypes (Salesforce Research)
This is a PyTorch implementation of the MoPro paper (Blog post):
@article{MoPro,
title={MoPro: Webly Supervised Learning with Momentum Prototypes},
author={Junnan Li and Caiming Xiong and Steven C.H. Hoi},
journal={ICLR},
year={2021}
}
### Requirements:
* WebVision dataset
* ImageNet dataset (for evaluation)
* Python ≥ 3.6
* PyTorch ≥ 1.4
### Training
This implementation currently only supports multi-gpu, DistributedDataParallel training, which is faster and simpler.
To perform webly-supervised training of a ResNet-50 model on WebVision V1.0 using a 4-gpu or 8-gpu machine, run:
python train.py \
--data [WebVision folder] \
--exp-dir experiment/MoPro\
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0
### Download MoPro Pre-trained ResNet-50 Models
WebVision V1| WebVision v2
------ | ------
### Noise Cleaning
python noise_cleaning.py --data [WebVision folder] --resume [pre-trained model path] --annotation pseudo_label.json
### Classifier Retraining on WebVision
python classifier_retrain.py --data [WebVision folder] --imagenet [ImageNet folder]\
--resume [pre-trained model path] --annotation pseudo_label.json --exp-dir experiment/cRT\
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0
### Fine-tuning on ImageNet (1% of labeled data)
python finetune_imagenet.py \
--data [ImageNet path] \
--model-path [pre-trained model path] \
--exp-dir experiment/Finetune \
--low-resource 0.01 \
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0
Result for WebVision-V1 pre-trained model:
Percentage | 1% | 10%
------ | ------ | ------
Accuracy | 71.2 | 74.8
### Linear SVM Evaluation on VOC or Places
python lowshot_svm.py --model_path [your pretrained model] --dataset VOC --voc-path [VOC data path]
Result for WebVision-V1 pre-trained model:
VOC| k=1 | k=2 | k=4 | k=8 | k=16
--- | --- | --- | --- | --- | ---
mAP| 59.5| 71.3| 76.5| 81.4| 83.7
Places| k=1 | k=2 | k=4 | k=8 | k=16
--- | --- | --- | --- | --- | ---
Acc| 16.9| 23.2| 29.2| 34.5| 38.7