Comments (2)
The article and code are consistent, and the two classifiers are combined into one classifier in the code.
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How time flies. It was posted almost three years ago.
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Related Issues (20)
- Section 3 Implementation details HOT 1
- load model from pretrain model error HOT 1
- Confusions about reverse sampler HOT 2
- aggregate feature HOT 1
- Multiplying by 2 when concatenate the mix feature?
- what is the mean of the "2× scheduler" in the section 5.4.2? HOT 3
- Accuracy on ImageNet-LT HOT 4
- how to get long-tail cifar HOT 1
- If I follow the default setting and run python main/train.py --cfg configs/cifar10.yaml, I cannot achieve similar results as reported in your paper.At the beginning, the loss of training set decreases, and the accuracy of verification set increases. However, after running for a period of time, the loss of training set will increase, and the accuracy of verification set will decrease, and fluctuate greatly. What's the matter? HOT 3
- Any improvement if BBN is utilized in object detection? HOT 1
- about validation
- How can I use this method in yolov5?
- Different hyperparameters between paper and code HOT 1
- is it possible that I implement this method using Keras? HOT 1
- Could you release the pretrained models for CIFAR?
- feature compactness calculation
- .
- about train2018.json HOT 1
- why I have a question that i never seen it, ABOUT shutil.Error HOT 1
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