# moco
**Repository Path**: dabaier/moco
## Basic Information
- **Project Name**: moco
- **Description**: PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-01
- **Last Updated**: 2024-10-22
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
## MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
This is a PyTorch implementation of the [MoCo paper](https://arxiv.org/abs/1911.05722):
```
@Article{he2019moco,
author = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick},
title = {Momentum Contrast for Unsupervised Visual Representation Learning},
journal = {arXiv preprint arXiv:1911.05722},
year = {2019},
}
```
It also includes the implementation of the [MoCo v2 paper](https://arxiv.org/abs/2003.04297):
```
@Article{chen2020mocov2,
author = {Xinlei Chen and Haoqi Fan and Ross Girshick and Kaiming He},
title = {Improved Baselines with Momentum Contrastive Learning},
journal = {arXiv preprint arXiv:2003.04297},
year = {2020},
}
```
### Preparation
Install PyTorch and ImageNet dataset following the [official PyTorch ImageNet training code](https://github.com/pytorch/examples/tree/master/imagenet).
This repo aims to be minimal modifications on that code. Check the modifications by:
```
diff main_moco.py <(curl https://raw.githubusercontent.com/pytorch/examples/master/imagenet/main.py)
diff main_lincls.py <(curl https://raw.githubusercontent.com/pytorch/examples/master/imagenet/main.py)
```
### Unsupervised Training
This implementation only supports **multi-gpu**, **DistributedDataParallel** training, which is faster and simpler; single-gpu or DataParallel training is not supported.
To do unsupervised pre-training of a ResNet-50 model on ImageNet in an 8-gpu machine, run:
```
python main_moco.py \
-a resnet50 \
--lr 0.03 \
--batch-size 256 \
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
[your imagenet-folder with train and val folders]
```
This script uses all the default hyper-parameters as described in the MoCo v1 paper. To run MoCo v2, set `--mlp --moco-t 0.2 --aug-plus --cos`.
***Note***: for 4-gpu training, we recommend following the [linear lr scaling recipe](https://arxiv.org/abs/1706.02677): `--lr 0.015 --batch-size 128` with 4 gpus. We got similar results using this setting.
### Linear Classification
With a pre-trained model, to train a supervised linear classifier on frozen features/weights in an 8-gpu machine, run:
```
python main_lincls.py \
-a resnet50 \
--lr 30.0 \
--batch-size 256 \
--pretrained [your checkpoint path]/checkpoint_0199.pth.tar \
--dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
[your imagenet-folder with train and val folders]
```
Linear classification results on ImageNet using this repo with 8 NVIDIA V100 GPUs :
|
pre-train
epochs |
pre-train
time |
MoCo v1
top-1 acc. |
MoCo v2
top-1 acc. |
| ResNet-50 |
200 |
53 hours |
60.8±0.2 |
67.5±0.1 |
Here we run 5 trials (of pre-training and linear classification) and report mean±std: the 5 results of MoCo v1 are {60.6, 60.6, 60.7, 60.9, 61.1}, and of MoCo v2 are {67.7, 67.6, 67.4, 67.6, 67.3}.
### Models
Our pre-trained ResNet-50 models can be downloaded as following:
### Transferring to Object Detection
See [./detection](detection).
### License
This project is under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for details.
### See Also
* [moco.tensorflow](https://github.com/ppwwyyxx/moco.tensorflow): A TensorFlow re-implementation.
* [Colab notebook](https://colab.research.google.com/github/facebookresearch/moco/blob/colab-notebook/colab/moco_cifar10_demo.ipynb): CIFAR demo on Colab GPU.