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---
library_name: tf-keras
tags:
- image-to-image
---
## Model description
This repo contains the model for the notebook [Image Classification using BigTransfer (BiT)](https://keras.io/examples/vision/bit/).
Full credits go to [Sayan Nath](https://twitter.com/sayannath2350)
Reproduced by [Rushi Chaudhari](https://github.com/rushic24)
BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification.
## Dataset
The [Flower Dataset](https://github.com/tensorflow/datasets/blob/master/docs/catalog/tf_flowers.md) is A large set of images of flowers
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
RESIZE_TO = 384
CROP_TO = 224
BATCH_SIZE = 64
STEPS_PER_EPOCH = 10
AUTO = tf.data.AUTOTUNE # optimise the pipeline performance
NUM_CLASSES = 5 # number of classes
SCHEDULE_LENGTH = (
500 # we will train on lower resolution images and will still attain good results
)
SCHEDULE_BOUNDARIES = [
200,
300,
400,
]
```
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).
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/).
### Training results
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