## 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/).

![table](https://i.imgur.com/oSaIBYZ.jpeg)

### Training results

![Metrics Image](./metrics.png)
{"library_name": "keras", "tags": ["image-to-image"]}
keras image-to-image arxiv:1912.11370 arxiv:1710.09412 has_space region:us
TAGS
#keras #image-to-image #arxiv-1912.11370 #arxiv-1710.09412 #has_space #region-us 

## Model description
This repo contains the model for the notebook Image Classification using BigTransfer (BiT).

Full credits go to Sayan Nath

Reproduced by Rushi Chaudhari

BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification.

## Dataset
The Flower Dataset is A large set of images of flowers

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:



The hyperparamteres like 'SCHEDULE_LENGTH' and 'SCHEDULE_BOUNDARIES' are determined based on empirical results. The method has been explained in the original paper and in their Google AI Blog Post.

The 'SCHEDULE_LENGTH' is aslo determined whether to use MixUp Augmentation or not. You can also find an easy MixUp Implementation in Keras Coding Examples.

!table

### Training results

!Metrics Image
## Model description
This repo contains the model for the notebook Image Classification using BigTransfer (BiT).

Full credits go to Sayan Nath

Reproduced by Rushi Chaudhari

BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification. ## Dataset
The Flower Dataset is A large set of images of flowers ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:



The hyperparamteres like 'SCHEDULE_LENGTH' and 'SCHEDULE_BOUNDARIES' are determined based on empirical results. The method has been explained in the original paper and in their Google AI Blog Post.

The 'SCHEDULE_LENGTH' is aslo determined whether to use MixUp Augmentation or not. You can also find an easy MixUp Implementation in Keras Coding Examples.

!table ### Training results

!Metrics Image
TAGS
#keras #image-to-image #arxiv-1912.11370 #arxiv-1710.09412 #has_space #region-us 
 ## Model description
This repo contains the model for the notebook Image Classification using BigTransfer (BiT).

Full credits go to Sayan Nath

Reproduced by Rushi Chaudhari

BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification. ## Dataset
The Flower Dataset is A large set of images of flowers ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:



The hyperparamteres like 'SCHEDULE_LENGTH' and 'SCHEDULE_BOUNDARIES' are determined based on empirical results. The method has been explained in the original paper and in their Google AI Blog Post.

The 'SCHEDULE_LENGTH' is aslo determined whether to use MixUp Augmentation or not. You can also find an easy MixUp Implementation in Keras Coding Examples.

!table ### Training results

!Metrics Image
passage: TAGS
#keras #image-to-image #arxiv-1912.11370 #arxiv-1710.09412 #has_space #region-us 
## Model description
This repo contains the model for the notebook Image Classification using BigTransfer (BiT).

Full credits go to Sayan Nath

Reproduced by Rushi Chaudhari

BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification.## Dataset
The Flower Dataset is A large set of images of flowers## Training procedure### Training hyperparameters

The following hyperparameters were used during training:



The hyperparamteres like 'SCHEDULE_LENGTH' and 'SCHEDULE_BOUNDARIES' are determined based on empirical results. The method has been explained in the original paper and in their Google AI Blog Post.

The 'SCHEDULE_LENGTH' is aslo determined whether to use MixUp Augmentation or not. You can also find an easy MixUp Implementation in Keras Coding Examples.

!table### Training results

!Metrics Image