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Chapter 22 · Huggingface Vision Trainer
Subchapter 22.3
references/image_classification_training_notebook.mdMarkdown11 KBView on GitHub
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:
To see all architectures and checkpoints compatible with this task, we recommend checking the task-page (opens in a new tab)
Before you begin, make sure you have all the necessary libraries installed:
pip install transformers datasets evaluate accelerate pillow torchvision scikit-learn trackioWe 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()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("ethz/food101", split="train[:5000]")Split the dataset’s train split into a train and test set with the train_test_split (opens in a new tab) method:
>>> food = food.train_test_split(test_size=0.2)Then take a look at an example:
>>> food["train"][0]
{'image': ,
'label': 79}Each example in the dataset has two fields:
image: a PIL image of the food itemlabel: the label class of the food itemTo 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)] = labelNow you can convert the label id to a label name:
>>> id2label[str(79)]
'prime_rib'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)Apply some image transformations to the images to make the model more robust against overfitting. Here you’ll use torchvision’s transforms (opens in a new tab) 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 examplesTo apply the preprocessing function over the entire dataset, use 🤗 Datasets with_transform (opens in a new tab) 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 (opens in a new tab). Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing such as padding.
>>> from transformers import DefaultDataCollator
>>> data_collator = DefaultDataCollator()Including a metric during training is often helpful for evaluating your model’s performance. You can quickly load an evaluation method with the 🤗 Evaluate (opens in a new tab) library. For this task, load the accuracy (opens in a new tab) metric (see the 🤗 Evaluate quick tour (opens in a new tab) 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 (opens in a new tab) 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.
If you aren’t familiar with finetuning a model with the Trainer (opens in a new tab), take a look at the basic tutorial here (opens in a new tab)!
You’re ready to start training your model now! Load ViT with AutoModelForImageClassification (opens in a new tab). 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:
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 (opens in a new tab) will evaluate the accuracy and save the training checkpoint.compute_metrics function.>>> 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_steps=0.1,
... logging_steps=10,
... report_to="trackio",
... run_name="food101",
... 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"],
... processing_class=image_processor,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()Once training is completed, share your model to the Hub with the push_to_hub() (opens in a new tab) method so everyone can use your model:
>>> trainer.push_to_hub()For a more in-depth example of how to finetune a model for image classification, take a look at the corresponding PyTorch notebook (opens in a new tab).
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("ethz/food101", split="validation[:10]")
>>> image = ds["image"][0]The simplest way to try out your finetuned model for inference is to use it in a pipeline() (opens in a new tab). 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:
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).logitsGet 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'