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Image classification

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Image classification

Image classification assigns a label or class to an image. Unlike text or audio classification, the inputs are the pixel values that comprise an image. There are many applications for image classification, such as detecting damage after a natural disaster, monitoring crop health, or helping screen medical images for signs of disease.

This guide illustrates how to:

  1. Fine-tune ViT on the Food-101 dataset to classify a food item in an image.
  2. Use your fine-tuned model for inference.

To see all architectures and checkpoints compatible with this task, we recommend checking the task-page

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate accelerate pillow torchvision scikit-learn

We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in:

>>> from huggingface_hub import notebook_login

>>> notebook_login()

Load Food-101 dataset

Start by loading a smaller subset of the Food-101 dataset from the 🤗 Datasets library. This will give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

>>> from datasets import load_dataset

>>> food = load_dataset("food101", split="train[:5000]")

Split the dataset’s train split into a train and test set with the train_test_split method:

>>> food = food.train_test_split(test_size=0.2)

Then take a look at an example:

>>> food["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F52AFC8AC50>,
 'label': 79}

Each example in the dataset has two fields:

  • image: a PIL image of the food item
  • label: the label class of the food item

To make it easier for the model to get the label name from the label id, create a dictionary that maps the label name to an integer and vice versa:

>>> labels = food["train"].features["label"].names
>>> label2id, id2label = dict(), dict()
>>> for i, label in enumerate(labels):
...     label2id[label] = str(i)
...     id2label[str(i)] = label

Now you can convert the label id to a label name:

>>> id2label[str(79)]
'prime_rib'

Preprocess

The next step is to load a ViT image processor to process the image into a tensor:

>>> from transformers import AutoImageProcessor

>>> checkpoint = "google/vit-base-patch16-224-in21k"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
Pytorch
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Apply some image transformations to the images to make the model more robust against overfitting. Here you’ll use torchvision’s transforms module, but you can also use any image library you like.

Crop a random part of the image, resize it, and normalize it with the image mean and standard deviation:

>>> from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor

>>> normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
>>> size = (
...     image_processor.size["shortest_edge"]
...     if "shortest_edge" in image_processor.size
...     else (image_processor.size["height"], image_processor.size["width"])
... )
>>> _transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])

Then create a preprocessing function to apply the transforms and return the pixel_values - the inputs to the model - of the image:

>>> def transforms(examples):
...     examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
...     del examples["image"]
...     return examples

To apply the preprocessing function over the entire dataset, use 🤗 Datasets with_transform method. The transforms are applied on the fly when you load an element of the dataset:

>>> food = food.with_transform(transforms)

Now create a batch of examples using DefaultDataCollator. Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing such as padding.

>>> from transformers import DefaultDataCollator

>>> data_collator = DefaultDataCollator()
TensorFlow
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To avoid overfitting and to make the model more robust, add some data augmentation to the training part of the dataset. Here we use Keras preprocessing layers to define the transformations for the training data (includes data augmentation), and transformations for the validation data (only center cropping, resizing and normalizing). You can use tf.imageor any other library you prefer.

>>> from tensorflow import keras
>>> from tensorflow.keras import layers

>>> size = (image_processor.size["height"], image_processor.size["width"])

>>> train_data_augmentation = keras.Sequential(
...     [
...         layers.RandomCrop(size[0], size[1]),
...         layers.Rescaling(scale=1.0 / 127.5, offset=-1),
...         layers.RandomFlip("horizontal"),
...         layers.RandomRotation(factor=0.02),
...         layers.RandomZoom(height_factor=0.2, width_factor=0.2),
...     ],
...     name="train_data_augmentation",
... )

>>> val_data_augmentation = keras.Sequential(
...     [
...         layers.CenterCrop(size[0], size[1]),
...         layers.Rescaling(scale=1.0 / 127.5, offset=-1),
...     ],
...     name="val_data_augmentation",
... )

Next, create functions to apply appropriate transformations to a batch of images, instead of one image at a time.

>>> import numpy as np
>>> import tensorflow as tf
>>> from PIL import Image


>>> def convert_to_tf_tensor(image: Image):
...     np_image = np.array(image)
...     tf_image = tf.convert_to_tensor(np_image)
...     # `expand_dims()` is used to add a batch dimension since
...     # the TF augmentation layers operates on batched inputs.
...     return tf.expand_dims(tf_image, 0)


>>> def preprocess_train(example_batch):
...     """Apply train_transforms across a batch."""
...     images = [
...         train_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
...     ]
...     example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
...     return example_batch


... def preprocess_val(example_batch):
...     """Apply val_transforms across a batch."""
...     images = [
...         val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
...     ]
...     example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
...     return example_batch

Use 🤗 Datasets set_transform to apply the transformations on the fly:

food["train"].set_transform(preprocess_train)
food["test"].set_transform(preprocess_val)

As a final preprocessing step, create a batch of examples using DefaultDataCollator. Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing, such as padding.

>>> from transformers import DefaultDataCollator

>>> data_collator = DefaultDataCollator(return_tensors="tf")

Evaluate

Including a metric during training is often helpful for evaluating your model’s performance. You can quickly load an evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

>>> import evaluate

>>> accuracy = evaluate.load("accuracy")

Then create a function that passes your predictions and labels to compute to calculate the accuracy:

>>> import numpy as np


>>> def compute_metrics(eval_pred):
...     predictions, labels = eval_pred
...     predictions = np.argmax(predictions, axis=1)
...     return accuracy.compute(predictions=predictions, references=labels)

Your compute_metrics function is ready to go now, and you’ll return to it when you set up your training.

Train

Pytorch
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If you aren’t familiar with finetuning a model with the Trainer, take a look at the basic tutorial here!

You’re ready to start training your model now! Load ViT with AutoModelForImageClassification. Specify the number of labels along with the number of expected labels, and the label mappings:

>>> from transformers import AutoModelForImageClassification, TrainingArguments, Trainer

>>> model = AutoModelForImageClassification.from_pretrained(
...     checkpoint,
...     num_labels=len(labels),
...     id2label=id2label,
...     label2id=label2id,
... )

At this point, only three steps remain:

  1. Define your training hyperparameters in TrainingArguments. It is important you don’t remove unused columns because that’ll drop the image column. Without the image column, you can’t create pixel_values. Set remove_unused_columns=False to prevent this behavior! The only other required parameter is output_dir which specifies where to save your model. You’ll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the Trainer will evaluate the accuracy and save the training checkpoint.
  2. Pass the training arguments to Trainer along with the model, dataset, tokenizer, data collator, and compute_metrics function.
  3. Call train() to finetune your model.
>>> training_args = TrainingArguments(
...     output_dir="my_awesome_food_model",
...     remove_unused_columns=False,
...     eval_strategy="epoch",
...     save_strategy="epoch",
...     learning_rate=5e-5,
...     per_device_train_batch_size=16,
...     gradient_accumulation_steps=4,
...     per_device_eval_batch_size=16,
...     num_train_epochs=3,
...     warmup_ratio=0.1,
...     logging_steps=10,
...     load_best_model_at_end=True,
...     metric_for_best_model="accuracy",
...     push_to_hub=True,
... )

>>> trainer = Trainer(
...     model=model,
...     args=training_args,
...     data_collator=data_collator,
...     train_dataset=food["train"],
...     eval_dataset=food["test"],
...     tokenizer=image_processor,
...     compute_metrics=compute_metrics,
... )

>>> trainer.train()

Once training is completed, share your model to the Hub with the push_to_hub() method so everyone can use your model:

>>> trainer.push_to_hub()
TensorFlow
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If you are unfamiliar with fine-tuning a model with Keras, check out the basic tutorial first!

To fine-tune a model in TensorFlow, follow these steps:

  1. Define the training hyperparameters, and set up an optimizer and a learning rate schedule.
  2. Instantiate a pre-trained model.
  3. Convert a 🤗 Dataset to a tf.data.Dataset.
  4. Compile your model.
  5. Add callbacks and use the fit() method to run the training.
  6. Upload your model to 🤗 Hub to share with the community.

Start by defining the hyperparameters, optimizer and learning rate schedule:

>>> from transformers import create_optimizer

>>> batch_size = 16
>>> num_epochs = 5
>>> num_train_steps = len(food["train"]) * num_epochs
>>> learning_rate = 3e-5
>>> weight_decay_rate = 0.01

>>> optimizer, lr_schedule = create_optimizer(
...     init_lr=learning_rate,
...     num_train_steps=num_train_steps,
...     weight_decay_rate=weight_decay_rate,
...     num_warmup_steps=0,
... )

Then, load ViT with TFAutoModelForImageClassification along with the label mappings:

>>> from transformers import TFAutoModelForImageClassification

>>> model = TFAutoModelForImageClassification.from_pretrained(
...     checkpoint,
...     id2label=id2label,
...     label2id=label2id,
... )

Convert your datasets to the tf.data.Dataset format using the to_tf_dataset and your data_collator:

>>> # converting our train dataset to tf.data.Dataset
>>> tf_train_dataset = food["train"].to_tf_dataset(
...     columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
... )

>>> # converting our test dataset to tf.data.Dataset
>>> tf_eval_dataset = food["test"].to_tf_dataset(
...     columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
... )

Configure the model for training with compile():

>>> from tensorflow.keras.losses import SparseCategoricalCrossentropy

>>> loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
>>> model.compile(optimizer=optimizer, loss=loss)

To compute the accuracy from the predictions and push your model to the 🤗 Hub, use Keras callbacks. Pass your compute_metrics function to KerasMetricCallback, and use the PushToHubCallback to upload the model:

>>> from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback

>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
>>> push_to_hub_callback = PushToHubCallback(
...     output_dir="food_classifier",
...     tokenizer=image_processor,
...     save_strategy="no",
... )
>>> callbacks = [metric_callback, push_to_hub_callback]

Finally, you are ready to train your model! Call fit() with your training and validation datasets, the number of epochs, and your callbacks to fine-tune the model:

>>> model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)
Epoch 1/5
250/250 [==============================] - 313s 1s/step - loss: 2.5623 - val_loss: 1.4161 - accuracy: 0.9290
Epoch 2/5
250/250 [==============================] - 265s 1s/step - loss: 0.9181 - val_loss: 0.6808 - accuracy: 0.9690
Epoch 3/5
250/250 [==============================] - 252s 1s/step - loss: 0.3910 - val_loss: 0.4303 - accuracy: 0.9820
Epoch 4/5
250/250 [==============================] - 251s 1s/step - loss: 0.2028 - val_loss: 0.3191 - accuracy: 0.9900
Epoch 5/5
250/250 [==============================] - 238s 949ms/step - loss: 0.1232 - val_loss: 0.3259 - accuracy: 0.9890

Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. You can now use it for inference!

For a more in-depth example of how to finetune a model for image classification, take a look at the corresponding PyTorch notebook.

Inference

Great, now that you’ve fine-tuned a model, you can use it for inference!

Load an image you’d like to run inference on:

>>> ds = load_dataset("food101", split="validation[:10]")
>>> image = ds["image"][0]
image of beignets

The simplest way to try out your finetuned model for inference is to use it in a pipeline(). Instantiate a pipeline for image classification with your model, and pass your image to it:

>>> from transformers import pipeline

>>> classifier = pipeline("image-classification", model="my_awesome_food_model")
>>> classifier(image)
[{'score': 0.31856709718704224, 'label': 'beignets'},
 {'score': 0.015232225880026817, 'label': 'bruschetta'},
 {'score': 0.01519392803311348, 'label': 'chicken_wings'},
 {'score': 0.013022331520915031, 'label': 'pork_chop'},
 {'score': 0.012728818692266941, 'label': 'prime_rib'}]

You can also manually replicate the results of the pipeline if you’d like:

Pytorch
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Load an image processor to preprocess the image and return the input as PyTorch tensors:

>>> from transformers import AutoImageProcessor
>>> import torch

>>> image_processor = AutoImageProcessor.from_pretrained("my_awesome_food_model")
>>> inputs = image_processor(image, return_tensors="pt")

Pass your inputs to the model and return the logits:

>>> from transformers import AutoModelForImageClassification

>>> model = AutoModelForImageClassification.from_pretrained("my_awesome_food_model")
>>> with torch.no_grad():
...     logits = model(**inputs).logits

Get the predicted label with the highest probability, and use the model’s id2label mapping to convert it to a label:

>>> predicted_label = logits.argmax(-1).item()
>>> model.config.id2label[predicted_label]
'beignets'
TensorFlow
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Load an image processor to preprocess the image and return the input as TensorFlow tensors:

>>> from transformers import AutoImageProcessor

>>> image_processor = AutoImageProcessor.from_pretrained("MariaK/food_classifier")
>>> inputs = image_processor(image, return_tensors="tf")

Pass your inputs to the model and return the logits:

>>> from transformers import TFAutoModelForImageClassification

>>> model = TFAutoModelForImageClassification.from_pretrained("MariaK/food_classifier")
>>> logits = model(**inputs).logits

Get the predicted label with the highest probability, and use the model’s id2label mapping to convert it to a label:

>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'beignets'
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