Create fcc_new_none.yaml

[tomer-grin]

Signed-off-by: tomer-grin 83909894+tomer-grin@users.noreply.github.com<!--
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Summary

🦷📄🚀

This pull request adds three YAML files to define different subsets of the dental fillings and crowns dataset, and modifies the main.py file to train and evaluate a YOLO model on one of the subsets using ClearML. The pull request aims to improve the model performance and simplify the data management.

YOLO for dentists
Training on new subsets -
Autumn leaves the None

Walkthrough

• Add three YAML files to define different subsets of the dental fillings and crowns dataset (data/fcc_new.yaml, data/fcc_new_reduced.yaml, data/fcc_new_none.yaml) (link, link, link)
• Update main.py to use the ultralytics YOLO module, ClearML library, and data/fcc_new_none.yaml for training and evaluation (link)
• Print the dataset files and comment out unused code in main.py (link)

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Added three new dataset configurations and a main script for YOLO training with ClearML integration.

📊 Key Changes

• 📁 Introduced fcc_new.yaml, fcc_new_none.yaml, and fcc_new_reduced.yaml files for new dataset configurations.
• 👩‍🏫 Each YAML configuration file specifies paths to training, validation (and optionally, test) image sets, along with class names relevant to dental imaging, like various types of fillings and crowns.
• 🚀 Added main.py, which includes code to initialize a simple YOLO model training pipeline. It utilizes ClearML for dataset version control and setting up the training task.

🎯 Purpose & Impact

• 🎓 These changes enable users to train YOLO models on specialized datasets, in this case, dental images.
• ⚙️ The new dataset configuration files allow for customization and clarify the structure needed for training.
• 🌐 ClearML integration facilitates experiment tracking, making it easier to manage and reproduce model training.
• 💡 The implemented script will help users get started with training custom YOLO models, improving accessibility for new learners and streamlining the process for professionals.

[github-actions]

👋 Hello @tomer-grin, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:

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