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---
title: CLIP Model Evaluation
emoji: 📊🚀
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 3.45.0
app_file: app.py
pinned: false
license: apache-2.0
---
# 📊 CLIP Model Evaluation Space
This Space provides an interactive interface to evaluate the performance of various CLIP (Contrastive Language-Image Pre-Training) models on standard image-text retrieval benchmarks.
It calculates Recall@K (R@1, R@5, R@10) metrics for both:
* **Image Retrieval (Text-to-Image):** Given a text query, how well does the model retrieve the correct image?
* **Text Retrieval (Image-to-Text):** Given an image query, how well does the model retrieve the correct text description?
## 🚀 How to Use
1. **Select a CLIP Model:** Choose a pre-trained CLIP model from the dropdown menu.
2. **Select a Dataset:** Choose the dataset you want to evaluate on (e.g., "mscoco", "flickr").
3. **Number of Samples:** Specify the number of image-text pairs from the dataset to use for the evaluation. Using fewer samples will be faster but less representative.
4. **Click "Evaluate Model":** The evaluation will run, and the Recall@K metrics will be displayed.
## 🛠️ Under the Hood
This Space uses the `evaluate` library from Hugging Face and a custom metric script (`clipmodel_eval.py`) to perform the CLIP model evaluations. The models and datasets are loaded from the Hugging Face Hub. |