Comments (5)
Hi, @yzou2,
Thanks for your kind words! I have reproduced his results (60.6 with 64 negatives) on ImageNet. I strictly followed his pre-training recipe (8GPUs, 256 batch size, 200 epochs, blabla), and used a Distributed Dataparallel training code (I will release this once I got some time).
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Thanks a lot, Yonglong! Hope to see the code soon.
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Hi @HobbitLong ,
Did you modify other parts than this distributed data parallel training (will this distributed training influence accuracy)? Could you please tell that If I directly change the configuration to (8Gpus, 256 batch size, 200 epochs) without changing current code, would this achieve comparable performance?
Thanks!
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Maybe this is a bit late, but just let you know that the DDP implementation for MoCo (as well as InstDis, MoCo v2, PIRL, ...) have been released here.
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Maybe this is a bit late, but just let you know that the DDP implementation for MoCo (as well as InstDis, MoCo v2, PIRL, ...) have been released here.
It's really great! Thanks!
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Related Issues (20)
- Weight shape in Linear classifier HOT 1
- Train/Test split when training linear classifier (ImageNet) HOT 3
- Gpu memory heavy on gpu0 HOT 1
- mean std value for LAB/YCbCr normalization HOT 1
- How to prevent positive sample is sampled by AliasMethod? HOT 1
- Format of dataset HOT 2
- index drawn by AliasMethod is not on the same gpu as the model HOT 1
- About the training epochs for ImageNet-1K
- question about shuffle_ids
- Training Loss Doesn't Improve for CIFAR10
- pretrained model of ResNet50v1
- Feature Tensor Shape Question HOT 1
- how to use the pretrained MoCo
- Protential memory leak when training?
- Modified AlexNet architecture for STL-10 dataset
- question about input image
- Questions about NCEAverage.py
- How can it be used for more than two views datasets?
- Pre-trained model for Video task HOT 1
- CMC for small custom dataset
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