Comments (7)
So far, we haven't tried the method on one-stage method.
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please where should I add the loss if I want to modify it on the one-stage model?
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Maybe you can try to let the anchor boxes predict multiple predictions.
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I'll try to implement a one-stage version based on RetinaNet and update the code if it works.
I think how to combine the Emd Loss and Focal Loss is the key to solve the problem.
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thx, hope your results.
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Applying emd loss to the Retinanet can also have gain in all evaluation indicators.
The result of a simple Retinanet version is as follows, and the code will be released soon.
retina_fpn
mAP:0.8180, mMR:0.5619, JI:0.7312
retina_emd
mAP:0.8303, mMR:0.5537, JI:0.7370
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The new code include the Retinanet implementation.
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Related Issues (20)
- 怎么在COCO数据集上进行训练 HOT 1
- [Question] Integrating the model to be used with FastMOT
- where is "init_weights = '/data/model/resnet50_fbaug.pth'" HOT 2
- no config.py in repo HOT 1
- setnms有gpu版本吗? HOT 4
- Multilabel single bounding box HOT 1
- AttributeError in training
- EMD移植到yolo出现重复框 HOT 10
- EMDLoss:取min问题 HOT 8
- 复现该idea之后的奇怪情况 HOT 3
- onnx and tensorrt
- Is there a pytorch version of Cascade RCNN EMD?
- 关于模型来源的问题
- Set NMS HOT 1
- Could you please release code trained with coco dataset or how can I convert coco to odgt format? Thank you. HOT 1
- 测试模型
- 预训练模型网盘链接失效 HOT 1
- citypersons数据集的配置问题 HOT 4
- 用VOC数据集
- NCCL error when running train.py
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