Overview: Detailed implementation of object detection using two of the most advanced and popular deep learning models in computer vision: YOLOv5 (You Only Look Once version 5) and Faster R-CNN (Faster Region-based Convolutional Neural Network).
Contents:
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Introduction and Setup:
- Overview of object detection and its applications.
- Setting up the environment and libraries needed for object detection (e.g., PyTorch, OpenCV).
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Loading and Preprocessing Data:
- Steps to load and preprocess images, potentially from a standard dataset like COCO.
- Image transformations such as resizing, normalization, and possibly augmentation techniques.
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Yolov5 Implementation:
- Loading a pre-trained YOLOv5 model.
- Running inference on test images and interpreting the results.
- Visualization of detection results (bounding boxes, class labels, confidence scores).
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Faster R-CNN Implementation:
- Loading and using a pre-trained Faster R-CNN model.
- Detailed explanation of how Faster R-CNN works, including Region Proposal Networks (RPNs) and the detection pipeline.
- Visualization of Faster R-CNN detection results.