vit_base_patch16_clip_224.openai Open Source Model - A Vision-Language Tool for Zero-Shot Image Classification

Vit Base Patch16 Clip 224.openai

Developed by timm

CLIP is a vision-language model developed by OpenAI, trained via contrastive learning for image and text encoders, supporting zero-shot image classification.

Text-to-Image

Transformers

Open Source License:Apache-2.0 #Zero-shot Image Classification #Multimodal Contrastive Learning #Vision-Text Alignment

Downloads 618.17k

Release Time : 11/1/2022

Model Overview

The CLIP model explores robustness factors in computer vision tasks and tests the model's ability to generalize to arbitrary image classification tasks in a zero-shot manner.

Model Features

Zero-shot Generalization Capability

Performs various image classification tasks without task-specific fine-tuning.

Multimodal Contrastive Learning

Jointly trains image and text encoders via contrastive loss.

Transformer Architecture

Utilizes ViT-B/16 visual transformer and text transformer encoders.

Model Capabilities

Zero-shot image classification

Image-text similarity computation

Cross-modal feature extraction

Use Cases

Academic Research

Computer Vision Robustness Study

Explores model performance on out-of-distribution data.

Demonstrates cross-dataset generalization in the paper.

Multimodal Learning Research

Investigates joint learning of vision and language representations.

Proves the effectiveness of contrastive learning.

🚀 CLIP (OpenAI model for timm)

The CLIP model developed by OpenAI researchers, aiming to explore robustness in computer vision tasks and test zero - shot generalization ability for arbitrary image classification.

🚀 Quick Start

This instance of the CLIP model is intended for loading in

timm (https://github.com/rwightman/pytorch-image-models) and
OpenCLIP (https://github.com/mlfoundations/open_clip) libraries.

Please see https://huggingface.co/openai/clip-vit-base-patch16 for use in Hugging Face Transformers.

✨ Features

The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks and test the zero - shot generalization ability for arbitrary image classification tasks. It's not for general model deployment.

📚 Documentation

Model Details

The CLIP model was developed by OpenAI researchers to study factors contributing to robustness in computer - vision tasks and test zero - shot generalization ability for arbitrary image classification. It's not for general deployment.

Model Date

January 2021

Model Type

Property	Details
Model Type	The model uses a ViT - B/16 Transformer architecture as an image encoder and 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 Transformer. This repository has the variant with the Vision Transformer.
Training Data	The model was trained on publicly available image - caption data, gathered by crawling websites and using pre - existing datasets like [YFCC100M](http://projects.dfki.uni - kl.de/yfcc100m/). The data is skewed towards more developed nations, younger, and male users.

Property

Details

Model Type

The model uses a ViT - B/16 Transformer architecture as an image encoder and 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 Transformer. This repository has the variant with the Vision Transformer.

Training Data

The model was trained on publicly available image - caption data, gathered by crawling websites and using pre - existing datasets like [YFCC100M](http://projects.dfki.uni - kl.de/yfcc100m/). The data is skewed towards more developed nations, younger, and male users.

Documents

Model Use

Intended Use

The model is a research output for research communities. It aims to help researchers understand and explore zero - shot, arbitrary image classification and can be used for interdisciplinary studies of potential impacts.

Primary intended uses

The primary users are AI researchers. The model is mainly used to understand the robustness, generalization, capabilities, biases, and constraints of computer - vision models.

Out - of - Scope Use Cases

⚠️ Important Note

Any deployed use case of the model (commercial or not) is currently out of scope. Non - deployed use cases like image search in a constrained environment are not recommended without thorough in - domain testing. Use cases related to surveillance and facial recognition are always out of scope. Also, since the model is only trained and evaluated in English, its use should be limited to English language cases.

Data

Data Mission Statement

The goal of building this dataset was to test robustness and generalizability in computer - vision tasks. The data was gathered from different public internet sources in a mostly non - interventionist way, filtering out violent and adult content. The dataset is not for commercial or deployed models and won't be released.

Limitations

CLIP and its analysis have limitations. CLIP struggles with tasks like fine - grained classification and object counting. It also has fairness and bias issues. Additionally, using linear probes to test CLIP may underestimate its performance.

Bias and Fairness

💡 Usage Tip

The performance and biases of CLIP depend on class design. Testing with the Fairface dataset showed significant disparities in race and gender, and these disparities can change based on class construction. CLIP achieved high accuracy in gender classification, ~93% in racial classification, and ~63% in age classification. Evaluations are for assessing performance and potential risks, not for endorsement.

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