This repository contains a fine-tuned YOLOv11 model for segmenting various car components. The model is trained on a Custom Dataset from Roboflow Universe and can be used to detect components such as hood, windows, doors, lights, etc in images.
The goal of this project is to create a deep learning model capable of accurately identifying and localizing distinct datapoints on hand. Leveraging YOLOv11's state-of-the-art architecture, the model can detect 21 distinct keypoints with high precision, making it suitable for use in gesture controls in Robotics and AR/VR.
The model was trained on the Ultralytics Hand-Keypoints Dataset, which includes 26,768 labeled images of hands in various lighting conditions, occlusions and surroundings.
The dataset contains a single class hand. All the keypoints are annotated within the bounding box for hand.
To use this project, ensure you have Python and the necessary dependencies installed. Follow these steps:
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Clone the repository:
git clone https://github.com/Tejax-v2/Hand-Keypoint-Detection.git cd Hand-Keypoint-Detection -
Install dependencies:
pip install -r requirements.txt
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Dataset Preparation: The dataset is downloaded on the go when using the configurations listed in data.yaml file, while starting the model training.
The model is trained using YOLOv11, with adjustments to hyperparameters specific to detecting keypoints.
Follow the notebook YOLOv11-Keypoint-Detection.ipynb for further instructions
- Dataset: ElectroCom61
- Reference: Roboflow
- Model: Ultralytics