optimizer & lr scheduler & loss function collections in PyTorch
RAdam implemented in Keras & TensorFlow
基于tf.keras的多标签多分类模型
Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay
A collection of deep learning models (PyTorch implemtation)
Object detection and instance segmentation on MaskRCNN with torchvision, albumentations, tensorboard and cocoapi. Supports custom coco datasets with positive/negative samples.
Pytorch implementation of lookahead optimizer(https://arxiv.org/pdf/1907.08610.pdf) and RAdam(https://arxiv.org/pdf/1908.03265.pdf)
Nadir: Cutting-edge PyTorch optimizers for simplicity & composability! 🔥🚀💻
Literature survey of convex optimizers and optimisation methods for deep-learning; made especially for optimisation researchers with ❤️
On The Variance Of The Adaptive Learning Rate And Beyond in tensorflow
MXNet implementation of RAdam optimizer
tf-keras-implemented YOLOv2
Ranger - a synergistic optimizer using RAdam (Rectified Adam) and Lookahead in one codebase
Classify known sites from around the world, given challenging and very big data set. This project is based on a kaggle competition.
Benchmarking Optimizers for Sign Language detection
python code, notebooks and Images used for AI502 Midterm Project.
Provide a collection of flexible learning rate schedulers for PyTorch with presets, warmup wrappers, and research-based implementations.
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