wehub-resource-sync
49 lines
1.9 KiB
Markdown
49 lines
1.9 KiB
Markdown
# Train YOLO26 Object Detection Model on a Custom Dataset
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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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---
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## Setup and installations
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**Get API Keys**:
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- [Roboflow](https://roboflow.com/) — needed to download the dataset. Store it as `ROBOFLOW_API_KEY` in a `.env` file.
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- [Comet ML](https://www.comet.com/) — needed for experiment tracking. Store it as `api_key` in a `.comet.config` file.
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Refer to `.env.example` and `.comet.config.example` files for the structure of the files.
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**Install Dependencies**:
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Ensure you have Python 3.12 or later installed.
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```bash
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uv sync
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```
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Select the above python virtual environment as kernel in the notebook.
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**Run the notebook**:
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Open and run `train_yolo26_object_detection.ipynb` end-to-end. The notebook covers:
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1. Pre-trained YOLO26 inference on a sample image
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2. Dataset download from Roboflow Universe (boxing-punch detection)
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3. Fine-tuning YOLO26 with Comet ML logging
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4. Validation on the best checkpoint
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5. Inference and annotated prediction on the test set
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---
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## 📬 Stay Updated with Our Newsletter!
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**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
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[](https://join.dailydoseofds.com)
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---
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## Contribution
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Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
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