There are 0 repository under loss topic.
Connectionist Temporal Classification (CTC) decoding algorithms: best path, beam search, lexicon search, prefix search, and token passing. Implemented in Python.
Official Pytorch Implementation of: "Asymmetric Loss For Multi-Label Classification"(ICCV, 2021) paper
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis
face recognition training project(pytorch)
遥感图像的语义分割,基于深度学习,在Tensorflow框架下,利用TF.Keras,运行环境TF2.0+
Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?
Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]
YOLOv4 Pytorch implementation with all freebies and specials and 15+ more exclusive improvements. Easy to use!
Prostate MR Image Segmentation 2012
An implementation for mnist center loss training and visualization
Deep Attentive Center Loss
Prostate MR Image Segmentation 2012
a simple pytorch implement of Multi-Sample Dropout
Weighted Focal Loss for multilabel classification
Implementation of "Anchor Loss: Modulating loss scale based on prediction difficulty"
Easy Custom Losses for Tree Boosters using Pytorch
A loss function for categories with a hierarchical structure.
IOU as loss for object detection tasks and IOU as metric for object detection tasks
pyIncore is a component of IN-CORE. It is a python package consisting of two primary components: 1) a set of service classes to interact with the IN-CORE web services, and 2) IN-CORE analyses . The pyIncore allows users to apply various hazards to infrastructure in selected areas, propagating the effect of physical infrastructure damage and loss of functionality to social and economic impacts.
Code of the ICASSP 2022 paper "Gradient Variance Loss for Structure Enhanced Super-Resolution"
Implementation of related angular-margin-based classification loss functions for training (face) embedding models: SphereFace, CosFace, ArcFace and MagFace.
An example crypto trading bot that does a trailing stop loss on Binance and posts to Slack
Software to visualize detectron training stats
This tool intents to help the network engineers (or anyone else) to analyze the path of the traffic via the Internet alayzing the tracroute collected with MTR against the information available in the public data sources.
Fast and differentiable MS-SSIM and SSIM for Paddle.
A tensorflow batteries included kit to write tensorflow networks from scratch or use existing ones.
GraLLAMA panel for LLAMA data