Train YOLO26 Object Detection Model on a Custom Dataset

This project demonstrates how to fine-tune a YOLO26m checkpoint on a custom object detection dataset using Ultralytics, download datasets directly from Roboflow Universe, and track experiments with Comet ML. The full workflow — from pre-trained inference to validation and test-set visualization — is covered in a single Jupyter notebook.

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Setup and installations

Get API Keys:

• Roboflow — needed to download the dataset. Store it as ROBOFLOW_API_KEY in a .env file.
• Comet ML — needed for experiment tracking. Store it as api_key in a .comet.config file.

Refer to .env.example and .comet.config.example files for the structure of the files.

Install Dependencies:

Ensure you have Python 3.12 or later installed.

uv sync

Select the above python virtual environment as kernel in the notebook.

Run the notebook:

Open and run train_yolo26_object_detection.ipynb end-to-end. The notebook covers:

1. Pre-trained YOLO26 inference on a sample image
2. Dataset download from Roboflow Universe (boxing-punch detection)
3. Fine-tuning YOLO26 with Comet ML logging
4. Validation on the best checkpoint
5. Inference and annotated prediction on the test set

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Contribution