A set of tools and toys to work with the Google TPU
| Tool | Good for |
|---|---|
| tpuparty-detection | Draw ROI of detections on a video frame |
Usage: tpuparty-detection [OPTIONS] SOURCE
Runs inference over source
Examples:
$ tpuparty "http://10.0.0.185/axis-cgi/mjpg/video.cgi?&camera=2"
$ tpuparty 0
Options:
--modeldir TEXT Directory containing the model weight and label
files [default: ~/models/detection/coco/]
-c, --confidence FLOAT Confidence threshold for object inference [default: 0.1]
--fps TEXT FPS playback for recordings
--version Show the version and exit.
--help Show this message and exit.tpuparty expects models to be presented in directories containing at least a
graph.tflite file like so:
├── classification/
│ └── efficientnet_l/
│ ├── efficientnet-edgetpu-L_quant_edgetpu.tflite
│ ├── graph.tflite -> efficientnet-edgetpu-L_quant_edgetpu.tflite
│ └── labels.txt
└── detection/
├── coco/
│ ├── graph.tflite
│ ├── labels.txt
│ └── README.md
└── google_coco/
├── graph.tflite
├── labels.txt
└── README.md
The common COCO trained mobilenet model and others are included in this repo for convenience.
Notice the models are sourced from various places and may have their own licences attached. The licence for this project pertains to the code only.
At this time tensorflow is not yet available for Python 3.8, so use the next best thing
$ mkvirtualenv -p python3.7 tpuparty
$ pip install .