Partial-FC
Partial FC is a distributed deep learning training framework for face recognition. The goal of Partial FC is to make training large scale classification task (eg. 10 or 100 millions identies) fast and easy. It is faster than model parallel and can train more identities.
Contents
Celeb-DeepGlint
We clean, merge, and release the largest and cleanest face recognition dataset Celeb-DeepGlint. Baseline models trained on Celeb-DeepGlint with our proposed training strategy can easily achieve state-of-the-art. The released dataset contains 18 million images of 360K individuals. The performance of Celeb-DeepGlint eval on large-scale test set IJB-C and Megaface are as follows:
Evaluation on IJB-C and Megaface
Our backbone is ResNet100, we set feature scale s to 64 and cosine margin m of CosFace at 0.4. TAR@FAR=1e-4 is reported on the IJB-C datasets, TAR@FAR=1e-6 is reported on Megaface verification.
| Test Dataset | IJB-C | Megaface_Id | Megaface_Ver |
|---|---|---|---|
| MS1MV2 | 96.4 | 98.3 | 98.6 |
| Celeb-DeepGlint | 97.3 | 99.1 | 99.1 |
Evaluation on IFRT
Coming soon.
Performance
We remove the influence of IO, all experiments use mixed precision training, backbone is ResNet50.
1 Million Identities On 8 RTX2080Ti
| Method | GPUs | BatchSize | Memory/M | Throughput img/sec | W |
|---|---|---|---|---|---|
| Model Parallel | 8 | 1024 | 10408 | 2390 | GPU |
| Partial FC(Ours) | 8 | 1024 | 8100 | 2780 | GPU |
10 Million Identities On 64 RTX2080Ti
| Method | GPUs | BatchSize | Memory/M | Throughput img/sec | W |
|---|---|---|---|---|---|
| Model Parallel | 64 | 2048 | 9684 | 4483 | GPU |
| Partial FC(Ours) | 64 | 4096 | 6722 | 12600 | GPU |
Citation
Coming soon.