Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR’s design improvements by implementing them in the MoCo framework. With simple modifications to MoCo—namely, using an MLP projection head and more data augmentation—we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible.
Predict image
from mmpretrain import inference_model
predict = inference_model('resnet50_mocov2-pre_8xb32-linear-steplr-100e_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])Use the model
import torch
from mmpretrain import get_model
model = get_model('mocov2_resnet50_8xb32-coslr-200e_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))Train/Test Command
Prepare your dataset according to the docs.
Train:
python tools/train.py configs/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.pyTest:
python tools/test.py configs/mocov2/benchmarks/resnet50_8xb32-linear-steplr-100e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/resnet50_linear-8xb32-steplr-100e_in1k/resnet50_linear-8xb32-steplr-100e_in1k_20220825-994c4128.pth| Model | Params (M) | Flops (G) | Config | Download |
|---|---|---|---|---|
mocov2_resnet50_8xb32-coslr-200e_in1k |
55.93 | 4.11 | config | model | log |
| Model | Pretrain | Params (M) | Flops (G) | Top-1 (%) | Config | Download |
|---|---|---|---|---|---|---|
resnet50_mocov2-pre_8xb32-linear-steplr-100e_in1k |
MOCOV2 | 25.56 | 4.11 | 67.50 | config | model | log |
@article{chen2020improved,
title={Improved baselines with momentum contrastive learning},
author={Chen, Xinlei and Fan, Haoqi and Girshick, Ross and He, Kaiming},
journal={arXiv preprint arXiv:2003.04297},
year={2020}
}