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Docs: Add GIoU loss
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maudzung committed Jul 24, 2020
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Expand Up @@ -16,7 +16,8 @@ The PyTorch Implementation based on YOLOv4 of the paper: [Complex-YOLO: Real-tim
- [x] Support [distributed data parallel training](https://github.com/pytorch/examples/tree/master/distributed/ddp)
- [x] Tensorboard
- [x] Mosaic/Cutout augmentation for training
- [ ] Use [CIoU](https://arxiv.org/pdf/1911.08287.pdf) / [GIoU](https://arxiv.org/pdf/1902.09630v2.pdf) loss for optimization.
- [x] Use [GIoU](https://arxiv.org/pdf/1902.09630v2.pdf) loss of rotated boxes for optimization.
- [ ] Use [CIoU](https://arxiv.org/pdf/1911.08287.pdf) loss of rotated boxes for optimization.

## 2. Getting Started
### 2.1. Requirement
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| |Backbone | Detector |
|---|---|---|
|**BoF** |[x] Dropblock <br> [x] Random rescale, rotation (global) <br> [x] Mosaic/Cutout augmentation|[x] Cross mini-Batch Normalization <br>[x] Dropblock <br> [x] Random training shapes <br> |
|**BoS** |[x] Mish activation <br> [x] Cross-stage partial connections (CSP) <br> [x] Multi-input weighted residual connections (MiWRC) |[x] Mish activation <br> [x] SPP-block <br> [x] SAM-block <br> [x] PAN path-aggregation block <br> [ ] CIoU/GIoU loss |
|**BoS** |[x] Mish activation <br> [x] Cross-stage partial connections (CSP) <br> [x] Multi-input weighted residual connections (MiWRC) |[x] Mish activation <br> [x] SPP-block <br> [x] SAM-block <br> [x] PAN path-aggregation block <br> [x] GIoU loss <br> [ ] CIoU loss |


## Contact
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