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预训练的模型的资源获取

预训练的模型的资源获取

作者: 可能性之兽 | 来源:发表于2022-07-15 11:39 被阅读0次

    集大成

    pretrained-models · GitHub Topics · GitHub

    DeepDA: https://github.com/jindongwang/transferlearning/tree/master/code/DeepDA
    DeepDG: https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG

    CV方向

    Tensorflow Hub

    TensorFlow Hub

    TIMM

    GitHub - rwightman/pytorch-image-models: PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

    用法:
    GitHub - rwightman/pytorch-image-models: PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

    import timm 
    import torch
    
    model = timm.create_model('resnet34')
    x     = torch.randn(1, 3, 224, 224)
    model.eval()
    model(x).shape
    
    
    ##or funting
    model.fc=nn.Linear(model.fc.in_features,n_classes) ##n_classes就是要调的
    

    NLP

    预训练模型的下载和使用 - 张耀灵的文章 - 知乎
    https://zhuanlan.zhihu.com/p/515599304

    huggingface transformers预训练模型如何下载至本地,并使用? - 于晨晨的文章 - 知乎
    https://zhuanlan.zhihu.com/p/147144376

    pytorch深度学习预训练模型pth下载 - 知乎 (zhihu.com)

    Huggingface
    Hugging Face – The AI community building the future.
    Hugging Face · GitHub

    >>> from transformers import AutoTokenizer, AutoModel
    
    >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
    >>> model = AutoModel.from_pretrained("bert-base-uncased")
    
    >>> inputs = tokenizer("Hello world!", return_tensors="pt")
    >>> outputs = model(**inputs)
    

    GitHub - ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT(中文BERT-wwm系列模型)

    其他

    因特尔的

    GitHub - openvinotoolkit/open_model_zoo: Pre-trained Deep Learning models and demos (high quality and extremely fast)

    主要是pytorch,但是有点老了
    GitHub - Cadene/pretrained-models.pytorch: Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

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