Object Detection using Tensor Flow. Computer Vision, Deep Learning.

Description:

This project demonstrates how to implement object detection using TensorFlow, leveraging computer vision and deep learning techniques. With the TensorFlow Object Detection API, it provides a modular and scalable approach for detecting objects in images and videos. Pre-trained models, such as those from the COCO dataset, can be used for quick deployment, while the framework also supports custom model training for specific use cases.

This system is ideal for applications in surveillance, autonomous vehicles, retail analytics, medical imaging, and more.

Features:

1. Pre-Trained Model Integration

   • Use TensorFlow's pre-trained models (e.g., SSD, Faster R-CNN) for quick object detection.

2. Custom Model Training

   • Train models on custom datasets for domain-specific applications.

3. Real-Time Detection

   • Perform object detection on live video feeds from webcams or other cameras.

4. Multi-Object Detection

   • Detect multiple objects and classify them simultaneously.

5. Configurable Thresholds

   • Adjust confidence thresholds for detection accuracy.

6. Annotation Support

   • Tools for annotating images for training custom models.

7. Visualizations

   • Bounding boxes, labels, and confidence scores displayed on images/videos.

8. Performance Optimization

   • Support for GPU acceleration for faster inference and training.

Requirements:

• Programming Language: Python 3.7 or higher
• Frameworks and Libraries:
  • TensorFlow 2.x
  • OpenCV
  • Matplotlib (for visualizations)
  • NumPy
  • PyYAML (for configuration parsing)
• Hardware:
  • GPU recommended (NVIDIA CUDA-supported GPU)
• Dataset:
  • Public datasets like COCO, Pascal VOC, or custom annotated datasets.

Instructions:

1. Install Dependencies

   • Create a virtual environment:

     bash

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     python -m venv env source env/bin/activate # For Windows: env\Scripts\activate

   • Install the required libraries:

     bash

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     pip install -r requirements.txt

2. Download Pre-Trained Models

   • Download pre-trained models from the TensorFlow Model Zoo:
     TensorFlow Model Zoo
   • Place the downloaded model in the models/ directory.

3. Configure the Project

   • Edit the config.py file to set paths for models, datasets, and outputs.
   • Adjust parameters like detection threshold, batch size, and image size.

4. Run Object Detection on Images

   • Use the provided script to detect objects in images:

     bash

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     python detect.py --input images/sample.jpg --output results/sample_detected.jpg

5. Real-Time Object Detection

   • Run the script for webcam or video feed detection:

     bash

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     python detect_realtime.py

6. Train Custom Models

   • Annotate your dataset using LabelImg or similar tools.
   • Generate TFRecords for training:

     bash

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     python generate_tfrecord.py --input datasets/annotations --output datasets/train.record

   • Train the custom model:

     bash

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     python model_main_tf2.py --pipeline_config_path=configs/ssd_custom.config --model_dir=models/custom