A treasure chest for visual classification and recognition powered by PaddlePaddle
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Updated
Aug 19, 2024 - Python
A treasure chest for visual classification and recognition powered by PaddlePaddle
Awesome Knowledge Distillation
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
"Effective Whole-body Pose Estimation with Two-stages Distillation" (ICCV 2023, CV4Metaverse Workshop)
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
Collection of AWESOME vision-language models for vision tasks
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
This is a collection of our NAS and Vision Transformer work.
Pytorch implementation of various Knowledge Distillation (KD) methods.
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
OpenMMLab Model Compression Toolbox and Benchmark.
Improving Convolutional Networks via Attention Transfer (ICLR 2017)
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
Efficient computing methods developed by Huawei Noah's Ark Lab
A curated list for Efficient Large Language Models
Collection of recent methods on (deep) neural network compression and acceleration.
EasyTransfer is designed to make the development of transfer learning in NLP applications easier.
The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf
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