openai/clip-vit-base-patch32
zero-shot-image-classification model by openai
From the publisher
Model card & documentation
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Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. January 2021 The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The original implementation had two variants: one using a ResNet image encoder and the other using a Vision…
Read the full model card ↗ · Preview checked 2026-10-27T23:19:53.067Z
Inside the original model card — Document outline
- Model Card: CLIP
- Model Details
- Model Date
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- Use with Transformers
- Model Use
- Intended Use
- Primary intended uses
- Out-of-Scope Use Cases
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- Data Mission Statement
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Model overview
openai/clip-vit-base-patch32 is a zero-shot-image-classification model repository published by openai on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- zero-shot-image-classification
- Library
- transformers
- Recent downloads (30 days)
- 17,678,010
- Cumulative likes
- 1,272
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- CLIPModel
- License
- Not reported — check the model card
- Access
- Not gated by Hugging Face
- Created
- 2022-03-02T23:29:05.000Z
- Last modified
- 2024-02-29T09:45:55.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:49:31.990Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.