University Project
For my computer vision and cognitive systems exam, I needed to develop a system capable of predicting manual gestures for tracking and stopping actions.
The goal was to fine-tune a pre-trained convolutional network, Yolov5, to accurately predict these gestures.
Action:
- I selected Yolov5, a network pre-trained on the Microsoft COCO dataset.
- I chose the Hagrid dataset for fine-tuning, which includes approximately 700GB of images with varying resolutions and qualities.
- I modified the network to retain the knowledge from its initial training while extending its capabilities for the new task.
This approach allowed the network to effectively map objects encountered during the tracking state, enhancing the robot’s capabilities without losing the knowledge gained from previous training.
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