CV Evaluations 🔎

cvevals is a framework for evaluating the results of computer vision models.

Roboflow evaluations is a Python package for evaluating computer vision models.

Using evaluations, you can:

1. Compare ground truth to a Roboflow model to benchmark and visualize model performance on images in bulk;
2. Test different Grounding DINO prompts to see which one most effectively annotates a specified class in an image;
3. Test different CLIP prompts to see which one most effectively classifies an image, and;
4. Evaluate resuts of different confidence levels for active learning.

Performance is measured using an aggregate of the following metrics:

• Precision
• Recall
• F1 Score

The following data formats are supported:

• YOLOv5 PyTorch TXT (object detection)
• Multiclass Classification TXT (classification)
• Classification Folder (classification)

Getting Started

To get started, clone the repository and install the required dependencies:

git clone https://github.com/roboflow/cvevals.git
cd cvevals
pip install -r requirements.txt
pip install -e .

Now you're ready to use this package!

Out of the box, we have created examples that let you evaluate the performance of the following models against your Roboflow datasets:

• CLIP (Classification)
• BLIP (Classification)
• ALBEF (Classification)
• Grounding DINO (Object Detection)
• DINOv2 and SVM (Classification)
• ImageBind (Classification)

License

This project is licensed under an MIT License.

Contributing

Interested in contributing to evaluations? Check out our contributing guidelines.