This set of Notebooks provides a complete set of code to be able to train and leverage your own custom object detection model using the Tensorflow Object Detection API.
This project guides you through building an object detection model for a specific category of objects using your own images and annotations. You'll learn how to:
Prepare your dataset with images and bounding box annotations. Generate TFRecord files for efficient training. Train a custom object detector on your data. Evaluate and visualize the performance of your model. Deploy your model for real-time inference. Target Audience: This project is suitable for beginners and intermediate users with basic Python knowledge and an interest in computer vision and deep learning.
Python 3.7+ TensorFlow 2.x (GPU recommended) NumPy OpenCV (optional) LabelImg (for image annotation)
Clone this repository: Bash git clone https://github.com/your-username/Nekoma_objectDetectionUsing-ML.git Use code with caution.
Install dependencies:
Bash pip install -r requirements.txt Use code with caution.
Prepare your dataset:
Place your images in the data/images folder. Annotate your images with bounding boxes using LabelImg or a similar tool. Save annotations in a format compatible with TensorFlow (e.g., PASCAL VOC). Follow the instructions in train.py to customize and train your model. This includes:
Selecting a pre-trained model as a starting point. Defining your custom object classes. Configuring training parameters. Running the training script. Once trained, use inference.py to test your model on new images or videos.
TensorFlow Object Detection API: https://tensorflow-object-detection-api-tutorial.readthedocs.io/ TensorFlow Custom Object Detection Tutorial: https://github.com/armaanpriyadarshan/Training-a-Custom-TensorFlow-2.X-Object-Detector LabelImg: https://github.com/topics/image-labeling
#Youtube Link Link : https://youtu.be/yuq_Y3Aj56Q