Image Classification

Use Cases

Image classification models can be used when we are not interested in specific instances of objects with location information or their shape.

Keyword Classification

Image classification models are used widely in stock photography to assign each image a keyword.

Image Search

Models trained in image classification can improve user experience by organizing and categorizing photo galleries on the phone or in the cloud, on multiple keywords or tags.

Inference

With the transformers library, you can use the image-classification pipeline to infer with image classification models. You can initialize the pipeline with a model id from the Hub. If you do not provide a model id it will initialize with google/vit-base-patch16-224 by default. When calling the pipeline you just need to specify a path, http link or an image loaded in PIL. You can also provide a top_k parameter which determines how many results it should return.

from transformers import pipeline
clf = pipeline("image-classification")
clf("path_to_a_cat_image")

[{'label': 'tabby cat', 'score': 0.731},
...
]

Useful Resources

• Let's Play Pictionary with Machine Learning!
• Fine-Tune ViT for Image Classification with 🤗Transformers
• Walkthrough of Computer Vision Ecosystem in Hugging Face - CV Study Group
• Computer Vision Study Group: Swin Transformer
• Computer Vision Study Group: Masked Autoencoders Paper Walkthrough
• Image classification task guide

Creating your own image classifier in just a few minutes

With HuggingPics, you can fine-tune Vision Transformers for anything using images found on the web. This project downloads images of classes defined by you, trains a model, and pushes it to the Hub. You even get to try out the model directly with a working widget in the browser, ready to be shared with all your friends!