CVevals 🔎

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, segmentation)
• Multiclass Classification TXT (multi-class classification)
• Classification Folder (single-class classification)

Results

Evaluators in the CVevals package return the precision, accuracy, and f1 score of model performance.

In addition, an output/ folder will be created with two subfolders:

1. images: Images with ground truth and predictions (only for object detection models)
2. matrices: Confusion matrices for each class. aggregate.png shows an aggregate confusion matrix for the model.

Here is an example confusion matrix from the output/matrices folder:

Here is an example of an image with ground truth and predictions from the output/images folder:

Prompt Comparison

When you compare two prompts and/or confidence levels using the CompareEvaluations.compare() function, the results will be printed to the console, like so:

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 CVevals!