**Tooth Detection and Numbering from Panoramic Radiography Using Artificial Neural Networks** 🚀 This repository contains the source code and resources for detecting and numbering teeth from panoramic radiography images. Developed as a graduate project at **T.C. Maltepe University**.

📚 Documentation

See See below for detailed information on setup, training, testing, and usage. See below for quickstart examples.

Training

The commands below demonstrate how to reproduce YOLOv5 [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/) results. Both [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) are downloaded automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are approximately 1/2/4/6/8 days on a single [NVIDIA V100 GPU](https://www.nvidia.com/en-us/data-center/v100/). Using [Multi-GPU training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) can significantly reduce training time. Use the largest `--batch-size` your hardware allows, or use `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). The batch sizes shown below are for V100-16GB GPUs. # Train the model on your dataset python train.py --data data/dataset.yaml --epochs 100 --weights '' --cfg yolov5s.pt --batch-size 16 # Train YOLOv5n on COCO for 300 epochs python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 # Train YOLOv5s on COCO for 300 epochs python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64 # Train YOLOv5m on COCO for 300 epochs python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40 # Train YOLOv5l on COCO for 300 epochs python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24 # Train YOLOv5x on COCO for 300 epochs python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16 ```

🧩 Integrations

Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management.

| Make Sense AI 🌟 | Weights & Biases | TensorBoard
| :-----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | | Streamline YOLO workflows: Label, train, and deploy effortlessly with Make Sense AI. Try now! | Track experiments, hyperparameters, and results with Weights & Biases. | Free forever, TensorBoard lets you save YOLO models, resume training, and interactively visualize predictions.

🤔 Why YOLOv5?

YOLOv5 is designed for simplicity and ease of use. We prioritize real-world performance and accessibility.

Pretrained Checkpoints for Dental Applications

This table highlights performance metrics for YOLOv5 models trained and fine-tuned on panoramic radiography datasets for tooth detection and numbering.

| Model | Size
^{(pixels) | mAPval
50-95 | Speed
CPU b1
(ms) | Speed
V100 b1
(ms) | Params
(M) | FLOPs
@640 (B) | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------|-----------------------|-------------------------------|-------------------------------|--------------------|-------------------------| | YOLOv5n | 640 | 32.0 | 50 | 8.0 | 1.9 | 4.5 | | YOLOv5s | 640 | 40.5 | 100 | 8.5 | 7.2 | 16.5 | | YOLOv5m | 640 | 48.0 | 230 | 10.5 | 21.2 | 49.0 | | YOLOv5l | 640 | 52.5 | 450 | 12.0 | 46.5 | 109.1 | | YOLOv5x | 640 | 54.0 | 800 | 15.0 | 86.7 | 205.7 |}

🏷️ Classification

YOLOv5 release v6.2 introduced support for image classification model training, validation, and deployment. Check the Release Notes for details and the YOLOv5 Classification Colab Notebook for quickstart guides.

Classification Usage Examples  

### Train YOLOv5 classification training supports automatic download for datasets like [MNIST](https://docs.ultralytics.com/datasets/classify/mnist/), [Fashion-MNIST](https://docs.ultralytics.com/datasets/classify/fashion-mnist/), [CIFAR10](https://docs.ultralytics.com/datasets/classify/cifar10/), [CIFAR100](https://docs.ultralytics.com/datasets/classify/cifar100/), [Imagenette](https://docs.ultralytics.com/datasets/classify/imagenette/), [Imagewoof](https://docs.ultralytics.com/datasets/classify/imagewoof/), and [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) using the `--data` argument. For example, start training on MNIST with `--data mnist`. ```bash # Train on a single GPU using CIFAR-100 dataset python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 # Train using Multi-GPU DDP on ImageNet dataset python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 ``` ### Val Validate the accuracy of the YOLOv5m-cls model on the ImageNet-1k validation dataset: ```bash # Download ImageNet validation split (6.3GB, 50,000 images) bash data/scripts/get_imagenet.sh --val # Validate the model python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 ``` ### Predict Use the pretrained YOLOv5s-cls.pt model to classify the image `bus.jpg`: ```bash # Run prediction python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg ``` ```python # Load model from PyTorch Hub model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt") ``` ### Export Export trained YOLOv5s-cls, ResNet50, and EfficientNet_b0 models to ONNX and TensorRT formats: ```bash # Export models python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 ```

☁️ Environments

Get started quickly with our pre-configured environments. Click the icons below for setup details.

📜 License

• AGPL-3.0 License: An OSI-approved open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the LICENSE file for details.

📧 Contact

For bug reports and feature requests related to YOLOv5, please visit GitHub Issues. For general questions, discussions, and community support, join our Discord server!