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@@ -78,20 +78,32 @@ Inference time was evaluated on Tesla V100 GPU, using onnxruntime-gpu==1.6.
## Rules
1. We have two tracks, determined by the size of training dataset.
* Track A: Use MS1M-V3 as training set.
* Track B: Use Glint360K as training set.
2. Training set and testing set are both already aligned to 112x112, re-alignment is prohibited.
3. Mask data-augmentation is allowed, such as [this](https://github.com/deepinsight/insightface/tree/master/recognition/tools). The tool you used should be reproducible.
1. We have two tracks, determined by the size of training dataset and inference time limitation.
* Track A: Use MS1M-V3 as training set, download: [ref-link](https://github.com/deepinsight/insightface/tree/master/challenges/iccv19-lfr)
* Track B: Use Glint360K as training set, download: [ref-link](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc)
2. Training set and testing set are both aligned to 112x112, re-alignment is prohibited.
3. Mask data-augmentation is allowed, such as [this](https://github.com/deepinsight/insightface/tree/master/recognition/tools). The applied mask augmentation tool should be reproducible.
4. External dataset and pretrained models are both prohibited.
5. Participants submit onnx model, then get scores by online evaluation. Test set is invisible.
5. Participants submit onnx model, then get scores by our online evaluation. Test images are invisible.
6. Matching score is measured by cosine similarity.
7. Model size should be not larger than 1GB.
8. For Track A: feature length should be not larger than 512, and the inference time should be not larger than 10ms on Tesla V100 GPU.
9. For Track B: feature length should be not larger than 1024, and the inference time should be not larger than 20ms on Tesla V100 GPU.
10. The input size of submission model should be 112x112.
7. Model size should not be larger than 1GB.
8. For Track A: feature length should not be not larger than 512, and the inference time should not be larger than 10ms on Tesla V100 GPU.
9. For Track B: feature length should not be not larger than 1024, and the inference time should not be larger than 20ms on Tesla V100 GPU.
10. The input shape of submission model should equal to 3x112x112 (RGB order).
11. Online evaluation server uses onnxruntime-gpu==1.6, cuda==10.2, cudnn==8.0.5.
12. Any float-16 model weights is prohibited, as it will lead to incorrect model size estimiation.
## Tutorial
1. ArcFace-PyTorch (with Partial-FC), [link](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch)
2. OneFlow, [link](https://github.com/deepinsight/insightface/tree/master/recognition/oneflow_face)
3. MXNet, [link](https://github.com/deepinsight/insightface/tree/master/recognition/ArcFace)
## Submission
Coming soon
1. Participants package the onnx model for submission using ``zip`` or ``tar -czf``.
2. Each participant can submit three times a day.
3. Please sign-up with the real organization name. You can hide the organization name in our system if you like.
4. You can decide which submission to be on the leaderboard by clicking the button.
Link coming soon