Aiy Vision Kit Utils
Libraries and examples for using the AIY Vision Kit with CogniFly (or any project based on the RPI Zero W)
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Rpi Vision | 75 | 4 years ago | 7 | mit | Python | |||||
| Tools and examples for getting started with object detection + classification tasks on Raspberry Pi, using Tensorflow 2.0 and Keras. READ ME FIRST: https://medium.com/@grepLeigh/portable-computer-vision-tensorflow-2-0-on-a-raspberry-pi-part-1-of-2-84e318798ce9 | ||||||||||
| Tensorflow Lite Rest Server | 74 | 5 months ago | 16 | apache-2.0 | Jupyter Notebook | |||||
| Expose tensorflow-lite models via a rest API using FastAPI | ||||||||||
| Tensorflow_lite_ssd_rpi_64 Bits | 23 | 2 years ago | 2 | bsd-3-clause | C++ | |||||
| TensorFlow Lite SSD on bare Raspberry Pi 4 with 64-bit OS at 24 FPS | ||||||||||
| Tensorflow_lite_pose_rpi_64 Bits | 15 | 2 years ago | bsd-3-clause | C++ | ||||||
| TensorFlow Lite Posenet on bare Raspberry Pi 4 with 64-bit OS at 9.4 FPS | ||||||||||
| Tensorflow_lite_segmentation_rpi_64 Bit | 14 | a year ago | bsd-3-clause | C++ | ||||||
| TensorFlow Lite segmentation on Raspberry Pi 4 aka Unet at 7.2 FPS with 64-bit OS | ||||||||||
| Image Recognition | 12 | 6 years ago | Python | |||||||
| a image recognition project use tenserflow with raspberry pi3. | ||||||||||
| Tensorflow_lite_ssd_rpi_32 Bits | 11 | 2 years ago | 1 | bsd-3-clause | C++ | |||||
| TensorFlow Lite SSD on a bare Raspberry Pi 4 at 17 FPS | ||||||||||
| Aiy Vision Kit Utils | 10 | 3 years ago | n,ull | gpl-3.0 | Jupyter Notebook | |||||
| Libraries and examples for using the AIY Vision Kit with CogniFly (or any project based on the RPI Zero W) | ||||||||||
| Pi Object Detection | 9 | 3 years ago | Shell | |||||||
| Raspberry Pi Object detection. | ||||||||||
| Self Driving Robot Using Neural Network | 9 | 3 years ago | mit | Python | ||||||
| This project introduces the autonomous robot which is a scaled down version of actual self-driving vehicle and designed with the help of neural network. The main focus is on building autonomous robot and train it on a designed track with the help of neural network so that it can run autonomously without a controller or driver on that specific track. The robot will stream the video to laptop which will then take decisions and send the data to raspberry pi which will then control the robot using motor driver. This motor driver will move the robot in required directions. Neural Network is used to train the model by first driving the robot on the specially designed track by labeling the images with the directions to be taken. After the model is trained it can make accurate predictions by processing the images on computer. This approach is better than conventional method which is done by extracting specific feature from images. | ||||||||||
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