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Unofficial Pytorch implementation of the paper 'Improved Baselines with Momentum Contrastive Learning' experiment on ImageNet-1K
Python 3.81%
Jupyter Notebook 96.19%
moco_v2's Introduction
MoCo v2 Implementation with Pytorch
- Unofficial implementation of the paper Improved Baselines with Momentum Contrastive Learning
- Check
env.txt
for more details
1. Implementation Details
- data.py : data augmentations, dataset
- main_linear.py : only train linear classifier with frozen backbone
- main_pretrain.py : pre-train backbone
- models_linear.py : model for linear probing
- models_moco.py : model for pre-train
- utils.py : utils such as scheduler, logger
- logs : log files
- visualize : visualize pretrain, linear probing log (acc, lr, train loss, val loss)
2. Linear Probing Result Comparison on ImageNet
Source |
Score |
Detail |
Paper |
67.7 |
200 epochs |
Paper |
71.1 |
800 epochs |
Current Repo |
67.41 |
200 epochs, query |
Current Repo |
67.50 |
200 epochs, key |
Current Repo |
70.12 |
800 epochs, query |
Current Repo |
70.15 |
800 epochs, key |
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