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TFLite Object Detection Android App Tutorial Using YOLOv5 | Yolov5 to tflite conversion
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Learn to Develop your Object Detection Mobile Application from yolov5 model.
Github:
Topics covered:
1- Train yolov5 model
2- Convert yolov5 (.pt model) into a tensorflow model(.pb file)
3- Convert tensorflow model (.pb model) to tflite model.
4- Download and install Android Studio
5- Build and run your Object detection App.
YOLOv5 belongs to the YOLO family of models. YOLO was initially introduced as the first object detection model that combined bounding box prediction and object classification into a single end to end differentiable network. It was written and is maintained in a framework called Darknet. YOLOv5 is the first of the YOLO models to be written in the PyTorch framework and it is much more lightweight and easy to use. That said, YOLOv5 did not make major architectural changes to the network in YOLOv4 and does not outperform YOLOv4 on a common benchmark, the COCO dataset.
Github:
Topics covered:
1- Train yolov5 model
2- Convert yolov5 (.pt model) into a tensorflow model(.pb file)
3- Convert tensorflow model (.pb model) to tflite model.
4- Download and install Android Studio
5- Build and run your Object detection App.
YOLOv5 belongs to the YOLO family of models. YOLO was initially introduced as the first object detection model that combined bounding box prediction and object classification into a single end to end differentiable network. It was written and is maintained in a framework called Darknet. YOLOv5 is the first of the YOLO models to be written in the PyTorch framework and it is much more lightweight and easy to use. That said, YOLOv5 did not make major architectural changes to the network in YOLOv4 and does not outperform YOLOv4 on a common benchmark, the COCO dataset.
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