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75 lines
2.6 KiB
Markdown
75 lines
2.6 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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## Results
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We employ ResNet100 as the backbone.
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### 1. IJB-C results
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| Datasets | 1e-05 | 1e-04 | 1e-03 | 1e-02 | 1e-01 |
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| :---: | :--- | :--- | :--- | :--- | :--- |
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| Glint360K | 95.92 | 97.30 | 98.13 | 98.78 | 99.28 |
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| MS1MV2 | 94.22 | 96.27 | 97.61 | 98.34 | 99.08 |
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### 2. IFRT results
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TODO
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## Training Speed Benchmark
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### 1. Train MS1MV2
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Employ **ResNet100** as the backbone.
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| GPU | FP16 | BatchSize / it | Throughput img / sec | Time / hours |
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| :--- | :--- | :--- | :--- | :--- |
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| 8 * Tesla V100-SXM2-32GB | False | 64 | 1658 | 15 |
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| 8 * Tesla V100-SXM2-32GB | True | 64 | 2243 | 12 |
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| 8 * Tesla V100-SXM2-32GB | False | 128 | 1800 | 14 |
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| 8 * Tesla V100-SXM2-32GB | True | 128 | 3337 | 7 |
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| 8 * RTX2080Ti | False | | 1200 | |
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| 8 * RTX2080Ti | | | | |
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Employ **ResNet50** as the backbone.
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| GPU | FP16 | BatchSize / it | Throughput img / sec | Time / hours |
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| :--- | :--- | :--- | :--- | :--- |
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| 8 * Tesla V100-SXM2-32GB | False | 64 | 2745 | 9 |
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| 8 * Tesla V100-SXM2-32GB | True | 64 | 3770 | 7 |
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| 8 * Tesla V100-SXM2-32GB | False | 128 | 2833 | 9 |
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| 8 * Tesla V100-SXM2-32GB | True | 128 | 5102 | 5 |
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### 2. Train millions classes
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TODO
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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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## 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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