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(CV)","自然语言处理 (NLP)","图神经网络","实用工具"],"sub_categories":[],"readme":"# Awesome-pytorch-list （ 精选 PyTorch 实用资源 ）\n\n## 自然语言处理 (NLP)\n\n- [transformers](https://github.com/huggingface/transformers): Huggingface 提供的最先进 NLP 模型库，支持 BERT、GPT 等模型。\n- [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py): 开源神经机器翻译工具。\n- [AllenNLP](https://github.com/allenai/allennlp): 基于 PyTorch 的 NLP 研究工具。\n- [neuralcoref](https://github.com/huggingface/neuralcoref): 基于 spaCy 的核心指代消解工具。\n- [fairseq](https://github.com/facebookresearch/fairseq): Facebook 提供的序列建模工具。\n- [pytext](https://github.com/facebookresearch/pytext): 自然语言建模框架。\n- [BERT-PyTorch](https://github.com/codertimo/BERT-pytorch): BERT 的 PyTorch 实现。\n\n---\n\n## 计算机视觉 (CV)\n\n- [torchvision](https://github.com/pytorch/vision): 提供预训练模型、图像数据集和数据增强工具。\n- [MMDetection](https://github.com/open-mmlab/mmdetection): 强大的目标检测工具箱。\n- [detectron2](https://github.com/facebookresearch/detectron2): 下一代目标检测平台。\n- [albumentations](https://github.com/albumentations-team/albumentations): 快速图像增强库。\n- [kornia](https://github.com/kornia/kornia): 可微分的计算机视觉库。\n- [facenet-pytorch](https://github.com/timesler/facenet-pytorch): 人脸检测和识别工具。\n- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): 语义分割工具箱。\n- [pytorch3d](https://github.com/facebookresearch/pytorch3d): 3D 数据深度学习库。\n\n---\n\n## 语音处理\n\n- [speechbrain](https://github.com/speechbrain/speechbrain): 基于 PyTorch 的开源语音工具包。\n- [espnet](https://github.com/espnet/espnet): 端到端语音处理工具。\n- [TTS](https://github.com/mozilla/TTS): 文本转语音工具。\n- [Tacotron2](https://github.com/NVIDIA/tacotron2): 高效文本到语音生成工具。\n\n---\n\n## 深度学习工具\n\n- [pytorch-lightning](https://github.com/Lightning-AI/lightning): 提高 PyTorch 代码可读性和训练效率的框架。\n- [ignite](https://github.com/pytorch/ignite): 高级训练工具库。\n- [fastai](https://github.com/fastai/fastai): 快速构建深度学习模型的工具。\n- [torchmetrics](https://github.com/PyTorchLightning/metrics): 模型评估指标工具。\n\n---\n\n## 图神经网络\n\n- [PyG](https://github.com/pyg-team/pytorch_geometric): 图深度学习扩展库。\n- [DGL](https://github.com/dmlc/dgl): 深度图学习框架。\n\n---\n\n## 推荐教程\n\n- [PyTorch 官方教程](https://pytorch.org/tutorials/): 官方提供的从入门到进阶的教程。\n- [pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial): 适合研究人员的 PyTorch 深度学习教程。\n- [Deep Learning with PyTorch](https://www.manning.com/books/deep-learning-with-pytorch): 《PyTorch 深度学习》书籍案例。\n- [Dive into Deep Learning (PyTorch)](https://github.com/dsgiitr/d2l-pytorch): 《动手学深度学习》PyTorch 版。\n\n---\n\n## 常见论文实现\n\n- [transformers](https://github.com/huggingface/transformers): Attention is All You Need 的实现。\n- [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix): 图像到图像翻译的经典实现。\n- [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix): 无监督图像到图像翻译。\n- [DeepSpeech2](https://github.com/SeanNaren/deepspeech.pytorch): 语音识别模型 DeepSpeech2 的实现。\n- [faster-rcnn.pytorch](https://github.com/jwyang/faster-rcnn.pytorch): Faster R-CNN 的 PyTorch 实现。\n\n---\n\n## 实用工具\n\n- [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch): TensorBoard 的 PyTorch 插件。\n- [torchsummary](https://github.com/sksq96/pytorch-summary): 模型结构和参数总结工具。\n- [torchstat](https://github.com/Swall0w/torchstat): 模型分析工具。\n- [pytorch-OpCounter](https://github.com/Lyken17/pytorch-OpCounter): 计算模型的 FLOPs。\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/xiaomingx%2Fawesome-pytorch-list/projects"}