• Tensorflow transfer learning EfficientNet on Stanford dogs

  • By: tensorflow
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Tensorflow transfer learning EfficientNet on Stanford dogs By cover art

Tensorflow transfer learning EfficientNet on Stanford dogs

By: tensorflow
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  • Summary

  • in Mandarin/zh, with EfficientNet, with pretrained weights, freeze all the weights, then you can add your top, then fit/train, you can reach satisfied accuracy with very limited training time/epochs; then unfreeze the layers -20: of non BN layers, change its trainable to True, then fit, you can get even better accuracy with very few like 10 epochs. So this is called transfer learning.
    tensorflow
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Episodes
  • OCR with ctc_layer to read captcha

    OCR with ctc_layer to read captcha
    Sep 11 2023

    OCR with ctc_loss_layer to read captchas

    带ctc损失层的keras OCR模型来读取网页上的captcha;

    训练模型最后是ctc_layer, 训练后模型不能直接用于推断，另外建立推断模型，使用原基础模型的输入 和 最后Dense层的输出 而建立新模型。推断结果也需要进行额外的解析
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    1 hr and 9 mins
  • keras Image Classification, cats and dogs 图片分类识别 猫和狗

    keras Image Classification, cats and dogs 图片分类识别 猫和狗
    Aug 10 2023

    in Mandarin/zh, the simplest way, keras Image Classification, build a model to classify cats and dogs, total two classes, that is, cat, dog. You download pics dataset of cats and dogs from microsoft, then apply augmentation ( translate, rotate, flip, contrast), build a residual convo model, compile and fit, save the model or keep the weights, now you can predict / inference if it is a cat or a dog. I took some pics of cats and dogs in the yard, nnnn, the result is not so good, I mean the accuracy is bad. Perhaps I shall let the cat/dog take major surface ratio like 80 -90% when shooting pics.
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    1 hr and 11 mins
  • transfer learning, use EfficientNet fine-tune to fit Stanford Dogs, in Mandarin

    transfer learning, use EfficientNet fine-tune to fit Stanford Dogs, in Mandarin
    Aug 8 2023

    transfer learning in Mandarin: 迁移学习，用预训练好的权重， EfficientNet, 不要top, 只有权重，冻结权重， 再建立自己的top, 训练后， 再把 后20层 非BN层的 解冻，改为可以训练权重， 再训练10个epo, 达到令人满意的准确度，而耗用的资源/计算力 非常有限
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    1 hr and 15 mins
Mathematics Science
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