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--- |
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tags: |
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- image-to-image |
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library_name: keras |
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--- |
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## Model description |
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This repo contains the model and the notebook [Image Classification using BigTransfer (BiT)](https://keras.io/examples/vision/bit/). |
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Full credits go to [Sayan Nath](https://twitter.com/sayannath2350) |
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Reproduced by [Rushi Chaudhari](https://github.com/rushic24) |
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BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification. |
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## Dataset |
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The [Flower Dataset](https://github.com/tensorflow/datasets/blob/master/docs/catalog/tf_flowers.md) is A large set of images of flowers |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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``` |
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RESIZE_TO = 384 |
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CROP_TO = 224 |
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BATCH_SIZE = 64 |
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STEPS_PER_EPOCH = 10 |
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AUTO = tf.data.AUTOTUNE # optimise the pipeline performance |
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NUM_CLASSES = 5 # number of classes |
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SCHEDULE_LENGTH = ( |
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500 # we will train on lower resolution images and will still attain good results |
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) |
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SCHEDULE_BOUNDARIES = [ |
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200, |
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300, |
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400, |
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] |
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``` |
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The hyperparamteres like `SCHEDULE_LENGTH` and `SCHEDULE_BOUNDARIES` are determined based on empirical results. The method has been explained in the [original paper](https://arxiv.org/abs/1912.11370) and in their [Google AI Blog Post](https://ai.googleblog.com/2020/05/open-sourcing-bit-exploring-large-scale.html). |
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The `SCHEDULE_LENGTH` is aslo determined whether to use [MixUp Augmentation](https://arxiv.org/abs/1710.09412) or not. You can also find an easy MixUp Implementation in [Keras Coding Examples](https://keras.io/examples/vision/mixup/). |
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### Training results |
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