Again details about how pretrained models are trained?
Open
Nobody has claimed this yet.
question
- Dominant language
- Python
- Stars
- 17.9k
- Forks
- 7.3k
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 13
Description
- I use v0.5.0/references to train a resnet50 wirth defalut config. But, I got Best_val Top1=75.806%, which has a gap of 0.3% about the pretrained model. How can I to repreduce your accuracy ?
- I notice you said you use you recompute the batch norm statistics after training, can you show more details?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with v0.5.0/references/classification/train.py and compare its default ResNet-50 training configuration with the pretrained model's reported accuracy. Document the steps needed to reproduce the reported Top-1 result and explain how batch-normalization statistics are recomputed after training; the issue is done when both procedures are clear and reproducible.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100