edge-tpu-tiny-yolo
xview-yolov3
| edge-tpu-tiny-yolo | xview-yolov3 | |
|---|---|---|
| 1 | 1 | |
| 100 | 245 | |
| - | 0.8% | |
| 1.8 | 6.3 | |
| almost 4 years ago | 3 days ago | |
| Python | Python | |
| MIT License | GNU Affero General Public License v3.0 |
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edge-tpu-tiny-yolo
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Deploying deep learning models on Raspberry Pi 4 B
The accelerator requires a tensorflow model. Here is someone's example to maybe get you going. Looks like some of the ops in the net aren't supported on the TPU, so you have to replace them with alternatives.
xview-yolov3
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How hard is this task - counting the number of cars from an aerial video clip
Use pretrained object detection models on aerial datasets. One of the datasets in xview dataset or DOTA dataset. You can use this repository : https://github.com/ultralytics/xview-yolov3
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
tensorflow-yolo-v3 - Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
pycoral - Python API for ML inferencing and transfer-learning on Coral devices
yolov4-custom-functions - A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.