Python Object Detection Insights

Documentation: here

Introduction

Are you ready to elevate your understanding of object detection datasets? Welcome to **Pyodi**, a simple tool designed to explore your object detection datasets effortlessly. The goal of this library is to provide intuitive visualizations, helping you to uncover the best parameters for generating a specific grid of anchors that can seamlessly fit your data characteristics.

Key Components

• Paint Annotations: Paints COCO format annotations and predictions.
• Ground Truth: Explore your dataset’s ground truth characteristics.
• Evaluation: Evaluates AP and AR between predictions and ground truth.
• Train Config Generation: Automatically generates anchors for your data.
• Train Config Evaluation: Evaluate the fitness between your data and your anchors.
• COCO Merge: Automatically merge COCO annotation files.
• COCO Split: Automatically split COCO annotation files into train and validation subsets.
• Crops Split: Creates a new dataset by splitting images into crops and adapting the annotations file.
• Crops Merge: Translates COCO ground truth or COCO predictions crops split into original image coordinates.

Installation

To get started with Pyodi, simply run the following command:

bash
pip install pyodi

Usage

Pyodi contains various applications that can help you extract the most from your dataset. You can download our TINY_COCO_ANIMAL dataset here to test the example commands. A classic flow could follow the following steps:

1. Annotation Visualization

With Pyodi’s paint_annotations tool, you can visualize your object detection dataset beautifully:

bash
pyodi paint-annotations $TINY_COCO_ANIMAL/annotations/train.json $TINY_COCO_ANIMAL/sample_images $TINY_COCO_ANIMAL/painted_images


2. Ground Truth Exploration

It’s crucial to explore your dataset in depth before training. The ground_truth app allows you to analyze your images and annotations for optimizing performance:

bash
pyodi ground-truth $TINY_COCO_ANIMAL/annotations/train.json




3. Train Config Generation

Creating the proper design of anchors is key to effective performance. Let Pyodi assist by automatically generating the right anchors:

bash
pyodi train-config generation $TINY_COCO_ANIMAL/annotations/train.json --input-size [1280,720] --n-ratios 3 --n-scales 3


4. Train Config Evaluation

Understand how well your anchors match your dataset with Pyodi’s evaluation app. It runs automatically after executing train-config generation, but can also be run independently:

bash
pyodi train-config evaluation $TINY_COCO_ANIMAL/annotations/train.json $TINY_COCO_ANIMAL/resources/anchor_config.py --input-size [1280,720]


Contributing

Your input can make a significant difference in enhancing Pyodi. Please refer to the Contributing guide for more information.

Troubleshooting

If you encounter any issues while using Pyodi, ensure you have Python 3.8 or higher installed, and verify that the necessary dependencies are properly set up. Also, ensure your dataset paths are correctly referenced in the commands. If problems persist, consider checking the official documentation or raising an issue on the GitHub repository.

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