Downloadclip-vit-large-patch14-336 is a vision-language model developed by OpenAI as part of the CLIP (Contrastive Language–Image Pre-training) family. It uses a Vision Transformer (ViT) backbone with 14×14 patch size and 336×336 image resolution to learn joint representations of images and text. Though detailed training data is undisclosed, the model was trained from scratch and enables powerful zero-shot classification by aligning visual and textual features in the same embedding space. Users can apply this model to perform tasks like zero-shot image recognition, image search with text, or text generation from visual cues—without task-specific training.
Features
- Vision Transformer architecture with 336×336 input resolution
- Supports zero-shot image classification and retrieval
- Joint image-text embedding space for multi-modal tasks
- Compatible with Hugging Face Transformers and PyTorch
- Fine-tunable for domain-specific vision-language tasks
- Base for many fine-tuned adapters and visual apps
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