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83 lines
2.4 KiB
Markdown
83 lines
2.4 KiB
Markdown
# Parital FC
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## TODO
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- [x] **No BUG** Sampling
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- [ ] Pytorch Experiments (Glint360k, 1.0/0.1)
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- [ ] Mixed precision training
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- [ ] Pipeline Parallel
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- [ ] Checkpoint
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- [ ] Docker
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- [ ] A Wonderful Documents
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## How to run
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cuda=10.1
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pytorch==1.6.0
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pip install -r requirement.txt
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```shell
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bash run.sh
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```
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使用 `bash run.sh` 这个命令运行。
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## Results
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There is a loss of accuracy in MS1MV2 with sampling,
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but it has the same good accuracy in Glint360k.
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This is what we found.
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**The more identities in the training set, the closer
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the results from the sampling training are to those from Full Softmax training!**
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### IJBC-Glint360K (mxnet)
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```shell script
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+--------------+-------+--------+-------+-------+-------+
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| Methods | 1e-05 | 0.0001 | 0.001 | 0.01 | 0.1 |
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+--------------+-------+--------+-------+-------+-------+
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| IJBC-1.0 | 95.92 | 97.30 | 98.13 | 98.78 | 99.28 |
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+--------------+-------+--------+-------+-------+-------+
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| IJBC-0.1 | 95.89 | 97.27 | 98.12 | 98.77 | 99.28 |
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+--------------+-------+--------+-------+-------+-------+
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```
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### IJBC-Glint360K (pytorch)
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```shell script
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+--------------+-------+--------+-------+-------+-------+
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| Methods | 1e-05 | 0.0001 | 0.001 | 0.01 | 0.1 |
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+--------------+-------+--------+-------+-------+-------+
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| IJBC-1.0 | | | | | |
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+--------------+-------+--------+-------+-------+-------+
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| IJBC-0.1 | | | | | |
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+--------------+-------+--------+-------+-------+-------+
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```
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### IJBC-MS1MV2 (pytorch)
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```shell script
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+--------------+-------+--------+-------+-------+-------+
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| Methods | 1e-05 | 0.0001 | 0.001 | 0.01 | 0.1 |
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+--------------+-------+--------+-------+-------+-------+
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| IJBC-1.0 | 94.22 | 96.27 | 97.61 | 98.34 | 99.08 |
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+--------------+-------+--------+-------+-------+-------+
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| IJBC-0.1 | 92.34 | 96.24 | 97.61 | 98.51 | 99.16 |
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+--------------+-------+--------+-------+-------+-------+
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```
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## Citation
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If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper:
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[Partial FC](https://arxiv.org/abs/2010.05222)
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```
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@inproceedings{an2020partical_fc,
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title={Partial FC: Training 10 Million Identities on a Single Machine},
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author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
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Zhang, Debing and Fu Ying},
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booktitle={Arxiv 2010.05222},
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year={2020}
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}
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```
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