参考:https://github.com/vandit15/Class-balanced-loss-pytorch 其中的class_balanced_loss.py: View Code 添加注释和输出的版本: View Code 返回: View Code 可见在代码中能够使用二分类求损失主要是因为将labels转换成了ont-hot格式 labels_one_hot = F.one_hot(labels, no_of_classes).float() 主要比较复...
Easy to use class balanced cross entropy and focal loss implementation for Pytorch python machine-learning computer-vision deep-learning pypi pytorch pip image-classification cvpr loss-functions cross-entropy focal-loss binary-crossentropy class-balanced-loss balanced-loss Updated Jan 27, 2023 Python ...
An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case. - AdeelH/pytorch-multi-class-focal-loss
Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra R-CNN: towards balanced learning for object detection. arXiv: 1904.02701 Pytorch,https://pytorch.org/ Ren M, Zeng W, Yang B, Urtasun R (2018) Learning to reweight examples for robust deep learning. In: International con...
An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case. - Initial commit · AdeelH/pytorch-multi-class-focal-loss@54b164e
we select a fixed size of subset within each class that has the minimized standard deviation of attributes as the Test-GBL. As long as Test-GBL is relatively more balanced in attributes than Train-CBL, it can serve as a valid testing set for ALT protocol. In summary,(Train-CBL, Test-GB...
Pre-training on balanced dataset, fine-tuning the last output layer before softmax on the original, imbalanced data. BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition (CVPR 2020) [Paper][Code] Class-Imbalanced Deep Learning via a Class-Balanced Ensemble (TNN...
Cui et al. [12] proposed a class-balanced loss function based on the effective number of samples. By calculating the effective number of samples for each class, this method determines the loss weights, further improving the traditional class weight methods and making the weighting of minority ...
Improvements were made to training speed, inference time, and model size in YOLOv5 [15]; additionally, user-friendly features were integrated through the PyTorch framework. This advancement facilitated more rapid and efficient object recognition, thereby broadening the range of practical applications for...
Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 2980–2988. [Google Scholar] Cui, Y.; Jia, M.; Lin, T.Y.; Song, Y.; Belongie, S. Class-balanced loss based on effective number of...