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CLIP (Contrastive Language-Image Pretraining) is a neural model that links images and text in a shared embedding space, allowing zero-shot image classification, similarity search, and multimodal alignment. It was trained on large sets of (image, caption) pairs using a contrastive objective: images and their matching text are pulled together in embedding space, while mismatches are pushed apart. Once trained, you can give it any text labels and ask it to pick which label best matches a given image—even without explicit training for that classification task. The repository provides code for model architecture, preprocessing transforms, evaluation pipelines, and example inference scripts. Because it generalizes to arbitrary labels via text prompts, CLIP is a powerful tool for tasks that involve interpreting images in terms of descriptive language.
Features
- Shared embedding space for images and text enabling zero-shot classification
- Model code for architecture, preprocessing, training, and inference
- Support for custom prompt templates and label embeddings
- Image/text similarity scoring and retrieval pipelines
- Example usage scripts and evaluation benchmarks
- Adaptation to new data or labels without retraining via prompt methods
Project Samples
