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Applying Image Recognition to Enhance Fisheries Management Capabilities
We need a Jupyter Notebook that can process a demo video to generate and display counts of objects (e.g., fish) detected in the video. This notebook will serve as a demonstration of our video analysis capabilities and should be user-friendly for new users to understand our process and results.
Differences between YOLOv2 (current) and YOLOv3 (also considered YOLOv6):
YOLOv3 uses three different scales or sizes of the detection grid, whereas YOLOv2 had only one scale. This allows YOLOv3 to detect objects of different sizes more effectively.
YOLOv3 utilizes residual blocks inspired by the ResNet architecture, enabling better feature extraction and representation.
YOLOv3 introduces skip connections, which facilitate the flow of information from earlier layers to later layers, aiding in the detection of objects at different scales.
Improved Accuracy and Performance: YOLOv3 (or YOLOv6) generally offers improved accuracy and performance compared to YOLOv2:
YOLOv3 achieves higher average precision and better localization accuracy due to its architectural improvements and multi-scale detection strategy.
YOLOv3 has a higher number of convolutional layers and more parameters, allowing it to capture more intricate features and improve detection accuracy.
YOLOv3 maintains real-time performance, although it may be slightly slower than YOLOv2 due to its increased complexity.
[Instructions for setting up and running the API locally or in a development environment]
[Instructions for deploying the API to production]
[Guidelines for contributing to the project]
[License information]
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