"""
Title: Image Classification using BigTransfer (BiT)
Author: [Sayan Nath](https://twitter.com/sayannath2350)
Date created: 2021/09/24
Last modified: 2024/01/03
Description: BigTransfer (BiT) State-of-the-art transfer learning for image classification.
Accelerator: GPU
Converted to Keras 3 by: [Sitam Meur](https://github.com/sitamgithub-MSIT)
"""
"""
## Introduction
BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image
classification. Transfer of pre-trained representations improves sample efficiency and
simplifies hyperparameter tuning when training deep neural networks for vision. BiT
revisit the paradigm of pre-training on large supervised datasets and fine-tuning the
model on a target task. The importance of appropriately choosing normalization layers and
scaling the architecture capacity as the amount of pre-training data increases.
BigTransfer(BiT) is trained on public datasets, along with code in
[TF2, Jax and Pytorch](https://github.com/google-research/big_transfer). This will help anyone to reach
state of the art performance on their task of interest, even with just a handful of
labeled images per class.
You can find BiT models pre-trained on
[ImageNet](https://image-net.org/challenges/LSVRC/2012/index) and ImageNet-21k in
[TFHub](https://tfhub.dev/google/collections/bit/1) as TensorFlow2 SavedModels that you
can use easily as Keras Layers. There are a variety of sizes ranging from a standard
ResNet50 to a ResNet152x4 (152 layers deep, 4x wider than a typical ResNet50) for users
with larger computational and memory budgets but higher accuracy requirements.
Figure: The x-axis shows the number of images used per class, ranging from 1 to the full
dataset. On the plots on the left, the curve in blue above is our BiT-L model, whereas
the curve below is a ResNet-50 pre-trained on ImageNet (ILSVRC-2012).
"""
"""
## Setup
"""
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import keras
from keras import ops
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
SEEDS = 42
keras.utils.set_random_seed(SEEDS)
"""
## Gather Flower Dataset
"""
train_ds, validation_ds = tfds.load(
"tf_flowers",
split=["train[:85%]", "train[85%:]"],
as_supervised=True,
)
"""
## Visualise the dataset
"""
plt.figure(figsize=(10, 10))
for i, (image, label) in enumerate(train_ds.take(9)):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(image)
plt.title(int(label))
plt.axis("off")
"""
## Define hyperparameters
"""
RESIZE_TO = 384
CROP_TO = 224
BATCH_SIZE = 64
STEPS_PER_EPOCH = 10
AUTO = tf.data.AUTOTUNE
NUM_CLASSES = 5
SCHEDULE_LENGTH = (
500
)
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/).
"""
"""
## Define preprocessing helper functions
"""
SCHEDULE_LENGTH = SCHEDULE_LENGTH * 512 / BATCH_SIZE
random_flip = keras.layers.RandomFlip("horizontal")
random_crop = keras.layers.RandomCrop(CROP_TO, CROP_TO)
def preprocess_train(image, label):
image = random_flip(image)
image = ops.image.resize(image, (RESIZE_TO, RESIZE_TO))
image = random_crop(image)
image = image / 255.0
return (image, label)
def preprocess_test(image, label):
image = ops.image.resize(image, (RESIZE_TO, RESIZE_TO))
image = ops.cast(image, dtype="float32")
image = image / 255.0
return (image, label)
DATASET_NUM_TRAIN_EXAMPLES = train_ds.cardinality().numpy()
repeat_count = int(
SCHEDULE_LENGTH * BATCH_SIZE / DATASET_NUM_TRAIN_EXAMPLES * STEPS_PER_EPOCH
)
repeat_count += 50 + 1
"""
## Define the data pipeline
"""
pipeline_train = (
train_ds.shuffle(10000)
.repeat(repeat_count)
.map(preprocess_train, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
pipeline_validation = (
validation_ds.map(preprocess_test, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
"""
## Visualise the training samples
"""
image_batch, label_batch = next(iter(pipeline_train))
plt.figure(figsize=(10, 10))
for n in range(25):
ax = plt.subplot(5, 5, n + 1)
plt.imshow(image_batch[n])
plt.title(label_batch[n].numpy())
plt.axis("off")
"""
## Load pretrained TF-Hub model into a `KerasLayer`
"""
bit_model_url = "https://tfhub.dev/google/bit/m-r50x1/1"
bit_module = hub.load(bit_model_url)
"""
## Create BigTransfer (BiT) model
To create the new model, we:
1. Cut off the BiT model’s original head. This leaves us with the “pre-logits” output.
We do not have to do this if we use the ‘feature extractor’ models (i.e. all those in
subdirectories titled `feature_vectors`), since for those models the head has already
been cut off.
2. Add a new head with the number of outputs equal to the number of classes of our new
task. Note that it is important that we initialise the head to all zeroes.
"""
class MyBiTModel(keras.Model):
def __init__(self, num_classes, module, **kwargs):
super().__init__(**kwargs)
self.num_classes = num_classes
self.head = keras.layers.Dense(num_classes, kernel_initializer="zeros")
self.bit_model = module
def call(self, images):
bit_embedding = self.bit_model(images)
return self.head(bit_embedding)
model = MyBiTModel(num_classes=NUM_CLASSES, module=bit_module)
"""
## Define optimizer and loss
"""
learning_rate = 0.003 * BATCH_SIZE / 512
lr_schedule = keras.optimizers.schedules.PiecewiseConstantDecay(
boundaries=SCHEDULE_BOUNDARIES,
values=[
learning_rate,
learning_rate * 0.1,
learning_rate * 0.01,
learning_rate * 0.001,
],
)
optimizer = keras.optimizers.SGD(learning_rate=lr_schedule, momentum=0.9)
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
"""
## Compile the model
"""
model.compile(optimizer=optimizer, loss=loss_fn, metrics=["accuracy"])
"""
## Set up callbacks
"""
train_callbacks = [
keras.callbacks.EarlyStopping(
monitor="val_accuracy", patience=2, restore_best_weights=True
)
]
"""
## Train the model
"""
history = model.fit(
pipeline_train,
batch_size=BATCH_SIZE,
epochs=int(SCHEDULE_LENGTH / STEPS_PER_EPOCH),
steps_per_epoch=STEPS_PER_EPOCH,
validation_data=pipeline_validation,
callbacks=train_callbacks,
)
"""
## Plot the training and validation metrics
"""
def plot_hist(hist):
plt.plot(hist.history["accuracy"])
plt.plot(hist.history["val_accuracy"])
plt.plot(hist.history["loss"])
plt.plot(hist.history["val_loss"])
plt.title("Training Progress")
plt.ylabel("Accuracy/Loss")
plt.xlabel("Epochs")
plt.legend(["train_acc", "val_acc", "train_loss", "val_loss"], loc="upper left")
plt.show()
plot_hist(history)
"""
## Evaluate the model
"""
accuracy = model.evaluate(pipeline_validation)[1] * 100
print("Accuracy: {:.2f}%".format(accuracy))
"""
## Conclusion
BiT performs well across a surprisingly wide range of data regimes
-- from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on
ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark
(VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class,
and 97.0% on CIFAR-10 with 10 examples per class.
You can experiment further with the BigTransfer Method by following the
[original paper](https://arxiv.org/abs/1912.11370).
**Example available on HuggingFace**
| Trained Model | Demo |
| :--: | :--: |
| [](https://huggingface.co/keras-io/bit) | [](https://huggingface.co/spaces/keras-io/siamese-contrastive) |
"""
