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  • 【coggle】模型加速下载

    网址

    https://coggle.club/note/dl/pretrained-models

    预训练模型镜像
    使用方法直接复制链接下载或者wget下载,链接在服务器直接下载。
    部分文件链接是http链接,请放心下载。

    NLTK

    http://www.nltk.org/nltk_data/

    百度云打包下载链接:https://pan.baidu.com/s/1Uc_8bSYb8K7vuDaVV7wNPw, 密码: m66f

    nltk_data/abc.zip
    nltk_data/alpino.zip
    nltk_data/averaged_perceptron_tagger.zip
    nltk_data/averaged_perceptron_tagger_ru.zip
    nltk_data/basque_grammars.zip
    nltk_data/biocreative_ppi.zip
    nltk_data/bllip_wsj_no_aux.zip
    nltk_data/book_grammars.zip
    nltk_data/brown.zip
    nltk_data/brown_tei.zip
    nltk_data/cess_cat.zip
    nltk_data/cess_esp.zip
    nltk_data/chat80.zip
    nltk_data/city_database.zip
    nltk_data/cmudict.zip
    nltk_data/comparative_sentences.zip
    nltk_data/comtrans.zip
    nltk_data/conll2000.zip
    nltk_data/conll2002.zip
    nltk_data/conll2007.zip
    nltk_data/crubadan.zip
    nltk_data/dependency_treebank.zip
    nltk_data/dolch.zip
    nltk_data/europarl_raw.zip
    nltk_data/floresta.zip
    nltk_data/gazetteers.zip
    nltk_data/genesis.zip
    nltk_data/gutenberg.zip
    nltk_data/ieer.zip
    nltk_data/inaugural.zip
    nltk_data/indian.zip
    nltk_data/jeita.zip
    nltk_data/kimmo.zip
    nltk_data/knbc.zip
    nltk_data/large_grammars.zip
    nltk_data/mac_morpho.zip
    nltk_data/machado.zip
    nltk_data/masc_tagged.zip
    nltk_data/maxent_ne_chunker.zip
    nltk_data/maxent_treebank_pos_tagger.zip
    nltk_data/moses_sample.zip
    nltk_data/movie_reviews.zip
    nltk_data/mte_teip5.zip
    nltk_data/mwa_ppdb.zip
    nltk_data/names.zip
    nltk_data/nombank.1.0.zip
    nltk_data/nonbreaking_prefixes.zip
    nltk_data/nps_chat.zip
    nltk_data/omw.zip
    nltk_data/opinion_lexicon.zip
    nltk_data/panlex_swadesh.zip
    nltk_data/paradigms.zip
    nltk_data/pe08.zip
    nltk_data/perluniprops.zip
    nltk_data/pil.zip
    nltk_data/pl196x.zip
    nltk_data/porter_test.zip
    nltk_data/ppattach.zip
    nltk_data/problem_reports.zip
    nltk_data/product_reviews_1.zip
    nltk_data/product_reviews_2.zip
    nltk_data/propbank.zip
    nltk_data/pros_cons.zip
    nltk_data/ptb.zip
    nltk_data/punkt.zip
    nltk_data/qc.zip
    nltk_data/reuters.zip
    nltk_data/rslp.zip
    nltk_data/rte.zip
    nltk_data/sample_grammars.zip
    nltk_data/semcor.zip
    nltk_data/senseval.zip
    nltk_data/sentence_polarity.zip
    nltk_data/sentiwordnet.zip
    nltk_data/shakespeare.zip
    nltk_data/sinica_treebank.zip
    nltk_data/smultron.zip
    nltk_data/snowball_data.zip
    nltk_data/spanish_grammars.zip
    nltk_data/state_union.zip
    nltk_data/stopwords.zip
    nltk_data/subjectivity.zip
    nltk_data/swadesh.zip
    nltk_data/switchboard.zip
    nltk_data/tagsets.zip
    nltk_data/timit.zip
    nltk_data/toolbox.zip
    nltk_data/treebank.zip
    nltk_data/twitter_samples.zip
    nltk_data/udhr.zip
    nltk_data/udhr2.zip
    nltk_data/unicode_samples.zip
    nltk_data/universal_tagset.zip
    nltk_data/universal_treebanks_v20.zip
    nltk_data/vader_lexicon.zip
    nltk_data/verbnet.zip
    nltk_data/verbnet3.zip
    nltk_data/webtext.zip
    nltk_data/wmt15_eval.zip
    nltk_data/word2vec_sample.zip
    nltk_data/wordnet.zip
    nltk_data/wordnet_ic.zip
    nltk_data/words.zip
    nltk_data/ycoe.zip

    keras

    https://github.com/fchollet/deep-learning-models/releases/

    densenet121_weights_tf_dim_ordering_tf_kernels.h5
    densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5
    densenet169_weights_tf_dim_ordering_tf_kernels.h5
    densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5
    densenet201_weights_tf_dim_ordering_tf_kernels.h5
    densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5
    NASNet-large-no-top.h5
    NASNet-large.h5
    NASNet-mobile-no-top.h5
    NASNet-mobile.h5
    inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5
    inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5
    mobilenet_1_0_128_tf.h5
    mobilenet_1_0_128_tf_no_top.h5
    mobilenet_1_0_160_tf.h5
    mobilenet_1_0_160_tf_no_top.h5
    mobilenet_1_0_192_tf.h5
    mobilenet_1_0_192_tf_no_top.h5
    mobilenet_1_0_224_tf.h5
    mobilenet_1_0_224_tf_no_top.h5
    mobilenet_2_5_128_tf.h5
    mobilenet_2_5_128_tf_no_top.h5
    mobilenet_2_5_160_tf.h5
    mobilenet_2_5_160_tf_no_top.h5
    mobilenet_2_5_192_tf.h5
    mobilenet_2_5_192_tf_no_top.h5
    mobilenet_2_5_224_tf.h5
    mobilenet_2_5_224_tf_no_top.h5
    mobilenet_5_0_128_tf.h5
    mobilenet_5_0_128_tf_no_top.h5
    mobilenet_5_0_160_tf.h5
    mobilenet_5_0_160_tf_no_top.h5
    mobilenet_5_0_192_tf.h5
    mobilenet_5_0_192_tf_no_top.h5
    mobilenet_5_0_224_tf.h5
    mobilenet_5_0_224_tf_no_top.h5
    mobilenet_7_5_128_tf.h5
    mobilenet_7_5_128_tf_no_top.h5
    mobilenet_7_5_160_tf.h5
    mobilenet_7_5_160_tf_no_top.h5
    mobilenet_7_5_192_tf.h5
    mobilenet_7_5_192_tf_no_top.h5
    mobilenet_7_5_224_tf.h5
    mobilenet_7_5_224_tf_no_top.h5
    inception_v3_weights_tf_dim_ordering_tf_kernels.h5
    inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
    xception_weights_tf_dim_ordering_tf_kernels.h5
    xception_weights_tf_dim_ordering_tf_kernels_notop.h5
    music_tagger_crnn_weights_tf_kernels_tf_dim_ordering.h5
    music_tagger_crnn_weights_tf_kernels_th_dim_ordering.h5
    inception_v3_weights_tf_dim_ordering_tf_kernels.h5
    inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
    inception_v3_weights_th_dim_ordering_th_kernels.h5
    inception_v3_weights_th_dim_ordering_th_kernels_notop.h5
    resnet50_weights_tf_dim_ordering_tf_kernels.h5
    resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
    resnet50_weights_th_dim_ordering_th_kernels.h5
    resnet50_weights_th_dim_ordering_th_kernels_notop.h5
    resnet50_weights_tf_dim_ordering_tf_kernels.h5
    resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
    resnet50_weights_th_dim_ordering_th_kernels.h5
    resnet50_weights_th_dim_ordering_th_kernels_notop.h5
    vgg16_weights_tf_dim_ordering_tf_kernels.h5
    vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
    vgg16_weights_th_dim_ordering_th_kernels.h5
    vgg16_weights_th_dim_ordering_th_kernels_notop.h5
    vgg19_weights_tf_dim_ordering_tf_kernels.h5
    vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5

    torchvision

    https://github.com/pytorch/vision

    alexnet-owt-4df8aa71.pth
    vgg11-bbd30ac9.pth
    vgg11_bn-6002323d.pth
    vgg13-c768596a.pth
    vgg13_bn-abd245e5.pth
    vgg16-397923af.pth
    vgg16_bn-6c64b313.pth
    vgg19-dcbb9e9d.pth
    vgg19_bn-c79401a0.pth
    resnet18-5c106cde.pth
    resnet18_fbgemm_16fa66dd.pth
    resnet34-333f7ec4.pth
    resnet50-19c8e357.pth
    resnet50_fbgemm_bf931d71.pth
    resnet101-5d3b4d8f.pth
    resnet152-b121ed2d.pth
    densenet121-a639ec97.pth
    densenet161-8d451a50.pth
    densenet169-b2777c0a.pth
    densenet201-c1103571.pth
    googlenet-1378be20.pth
    googlenet_fbgemm-c00238cf.pth
    inception_v3_google-1a9a5a14.pth
    inception_v3_google_fbgemm-71447a44.pt
    mnasnet0.5_top1_67.823-3ffadce67e.pth
    mnasnet1.0_top1_73.512-f206786ef8.pth
    mobilenet_v2-b0353104.pth
    mobilenet_v2_qnnpack_37f702c5.pt
    resnext101_32x8_fbgemm_09835ccf.pth
    resnext101_32x8d-8ba56ff5.pth
    resnext50_32x4d-7cdf4587.pth
    shufflenetv2_x0.5-f707e7126e.pth
    shufflenetv2_x1-5666bf0f80.pth
    shufflenetv2_x1_fbgemm-db332c57.pth
    squeezenet1_0-a815701f.pth
    squeezenet1_1-f364aa15.pth
    deeplabv3_resnet101_coco-586e9e4e.pth
    deeplabv3_resnet50_coco-cd0a2569.pth
    fcn_resnet101_coco-7ecb50ca.pth
    fcn_resnet50_coco-1167a1af.pth
    fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
    retinanet_resnet50_fpn_coco-eeacb38b.pth
    keypointrcnn_resnet50_fpn_coco-9f466800.pth
    keypointrcnn_resnet50_fpn_coco-fc266e95.pth
    maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
    mc3_18-a90a0ba3.pth
    r2plus1d_18-91a641e6.pth
    r3d_18-b3b3357e.pth
    wide_resnet101_2-32ee1156.pth
    wide_resnet50_2-95faca4d.pth

    pretrained-models.pytorch

    https://github.com/Cadene/pretrained-models.pytorch

    EfficientNet-PyTorch

    https://github.com/lukemelas/EfficientNet-PyTorch

    efficientnet-b0-355c32eb.pth
    efficientnet-b1-f1951068.pth
    efficientnet-b2-8bb594d6.pth
    efficientnet-b3-5fb5a3c3.pth
    efficientnet-b4-6ed6700e.pth
    efficientnet-b5-b6417697.pth
    efficientnet-b6-c76e70fd.pth
    efficientnet-b7-dcc49843.pth

    tensorflow-models

    https://github.com/tensorflow/models

    darknet

    https://pjreddie.com/darknet/

    yolov2-tiny.weights
    yolov2.weights
    yolov3-tiny.weights
    yolov3.weights
    yolov3-spp.weights

    mmdetection

    https://github.com/open-mmlab/mmdetection

    insightface

    https://github.com/deepinsight/insightface

    model-MobileFaceNet-arcface-ms1m-refine-v1.zip
    model-r34-arcface-ms1m-refine-v1.zip
    model-r50-arcface-ms1m-refine-v1.zip
    model-r100-arcface-ms1m-refine-v2.zip
    ssh-model-final.zip

    bert

    https://github.com/google-research/bert

    chinese_L-12_H-768_A-12.zip
    https://github.com/brightmart/roberta_zh

    https://github.com/ymcui/Chinese-BERT-wwm

    chinese_bert_chinese_wwm_L-12_H-768_A-12.zip
    chinese_roberta_wwm_ext_L-12_H-768_A-12.zip
    chinese_wwm_ext_L-12_H-768_A-12.zip
    chinese_roberta_wwm_large_ext_L-24_H-1024_A-16.zip
    https://github.com/google-research/albert

    albert_base_zh.tar.gz
    albert_large_zh.tar.gz
    albert_xlarge_zh.tar.gz
    albert_xxlarge_zh.tar.gz
    https://github.com/huggingface/transformers

    https://github.com/dbiir/UER-py/

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  • 原文地址:https://www.cnblogs.com/xiximayou/p/14748518.html
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