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Clip Rsicd

Developed by flax-community
A remote sensing image-specific model fine-tuned based on OpenAI CLIP, enhancing zero-shot classification and image retrieval capabilities
Downloads 146
Release Time : 3/2/2022

Model Overview

This model is optimized for remote sensing images, excelling in zero-shot image classification, text-to-image, and image-to-image retrieval tasks

Model Features

Optimized for Remote Sensing Images
Fine-tuned on datasets like RSICD, significantly improving understanding of aerial/satellite images
Zero-shot Classification Capability
Performs image classification on new categories without fine-tuning, achieving 84.3% Top-1 accuracy
Cross-modal Retrieval
Supports bidirectional retrieval between text-to-image and image-to-image
Efficient Training
Training accelerated with TPU-v3-8, complete training scripts and logs provided

Model Capabilities

Zero-shot image classification
Text-to-image retrieval
Image-to-image retrieval
Remote sensing image understanding

Use Cases

Research Applications
Computer Vision Research
Investigating the robustness of zero-shot learning and cross-modal representations
Industry Applications
Environmental Monitoring
Automatically identifying changes in ecological areas like forests and water bodies
Urban Planning
Classifying urban functional zones such as residential and commercial areas
47% accuracy improvement over original CLIP
Disaster Assessment
Rapid retrieval of disaster-affected area images
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