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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:

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 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

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Inference API
Inference API (serverless) does not yet support keras models for this pipeline type.

Space using keras-io/bit 1