Resnet50_clip.yfcc15m Open Source Model - Free Support for Zero-shot Image Classification Tasks

Resnet50 Clip.yfcc15m

Developed by timm

ResNet50 model trained on the YFCC-15M dataset, compatible with both open_clip and timm frameworks, supporting zero-shot image classification tasks.

Image Classification

Safetensors

Open Source License:MIT #Zero-shot classification #Dual-framework compatibility #Quick GELU activation

Downloads 631

Release Time : 10/23/2024

Model Overview

This model is a dual-purpose vision model, known as RN50-quickgelu in OpenCLIP and resnet50_clip.yfcc15m in timm, primarily used for zero-shot image classification tasks.

Model Features

Dual-framework compatibility

Compatible with both open_clip and timm frameworks, offering more flexible usage options.

Zero-shot learning capability

Supports zero-shot image classification, enabling classification without specific category training data.

Quick activation function

Uses quickgelu activation function, potentially providing faster training and inference speeds.

Model Capabilities

Zero-shot image classification

Image feature extraction

Use Cases

Computer Vision

General image classification

Classify images of arbitrary categories without specific training.

Visual content analysis

Extract image features for content understanding and analysis.

Property	Details
Model Type	Dual use model for `open_clip` and `timm`
Training Data	YFCC - 15M

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Resnet50 Clip.yfcc15m

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Model card for resnet50_clip.yfcc15m

🚀 Quick Start

📚 Documentation

Model Details

Model Usage

📄 License