---
title: Detection Metrics
emoji: 📈
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 3.36.1
#app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference


# COCO Metrics

COCO Metrics is a Python package that provides evaluation metrics for **object detection** tasks using the COCO (Common Objects in Context) [evaluation protocol](https://cocodataset.org/#detection-eval). In the future instance segmentation tasks will also be supported.

## Advantages

* This project does not depend directly on [pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools), COCO's official code to compute metrics.  
* It does not require a `.json` file in disk, as originally required by pycocotools.  
* Integrated with HuggingFace 🤗 evaluate model.  

## Metrics

The following 12 metrics are computed to for characterizing the performance of an object detector:
* **Average Precision** (AP) IoU=.50:.05:.95
* **Average Precision** (AP) IoU=.50 
* **Average Precision** (AP) IoU=.75
* **Average Precision** (AP) Across Scales for small objects: area < 322 
* **Average Precision** (AP) Across Scales for medium objects: 322 < area < 962 
* **Average Precision** (AP) Across Scales for large objects: area < 962 
* **Average Recall** (AR) given 1 detection per image 
* **Average Recall** (AR) given 10 detections per image 
* **Average Recall** (AR) given 100 detections per image
* **Average Recall** (AR) for small objects: area < 322 
* **Average Recall** (AR) for medium objects: 322 < area < 962 
* **Average Recall** (AR) for large objects: area > 962

## Installation
COCO Metrics can be easily installed using pip:

```
# Clone the project
git clone https://github.com/rafaelpadilla/coco_metrics
cd coco_metrics

# Create environment 
conda create -n coco-metrics python=3.10
conda activate coco-metrics

# Install packages
pip install -r requirements.txt
```

## Example

The code [example.py](https://github.com/rafaelpadilla/coco_metrics/blob/main/example.py) shows how to make usage of the COCO evaluator using HuggingFace 🤗 evaluate model.

The snippet below illustrates how to make call of the evaluator.

```
import evaluate

# Load evaluate from github
coco_bbx_evaluator = evaluate.load("rafaelpadilla/detection_metrics", coco=coco_gt, iou_type="bbox")

# Within your dataset looping, you add the metrics
for batch in dataloader:

    results = # model compute predicted results
    labels = # get ground-truth labels

    # Add prediction and expected labels to the evaluator
    coco_bbx_evaluator.add(prediction=results, reference=labels)

# Compute the metrics and show results
results = coco_bbx_evaluator.compute()
print(results)

```

## References:

[1] [COCO Metrics](https://cocodataset.org/#detection-eval)  
[2] [A Survey on Performance Metrics for Object-Detection Algorithms](https://www.researchgate.net/profile/Rafael-Padilla/publication/343194514_A_Survey_on_Performance_Metrics_for_Object-Detection_Algorithms/links/5f1b5a5e45851515ef478268/A-Survey-on-Performance-Metrics-for-Object-Detection-Algorithms.pdf)  
[3] [A comparative analysis of object detection metrics with a companion open-source toolkit](https://www.researchgate.net/profile/Rafael-Padilla/publication/343194514_A_Survey_on_Performance_Metrics_for_Object-Detection_Algorithms/links/5f1b5a5e45851515ef478268/A-Survey-on-Performance-Metrics-for-Object-Detection-Algorithms.pdf)