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README.md

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+<p align="center">
+  <img src="docs/cannine.png" alt="Project Logo" width="100"/>
+</p>
+
+# caNNine
+A lot of people walk some very friendly dogs past my house throughout the day. Using some inference-on-the-edge and finetuned ResNet models (the **N**eural **N**etworks in ca**NN**ine) I can be alerted to their presence and get some seratonin. 
+
+<div align="center">
+<img width="55" src="https://raw.githubusercontent.com/gilbarbara/logos/master/logos/python.svg"/>
+<img width="55" src="https://raw.githubusercontent.com/gilbarbara/logos/master/logos/pytorch-icon.svg"/>
+<img width="100" src="https://upload.wikimedia.org/wikipedia/commons/4/45/OpenVINO_logo.svg"/>
+<img width="55" src="https://raw.githubusercontent.com/gilbarbara/logos/master/logos/flask.svg"/>
+<img width="55" src="https://raw.githubusercontent.com/gilbarbara/logos/master/logos/raspberry-pi.svg"/>
+</div>
+
+# Setup
+The first part of the system uses a [Luxonis Oak-1 Lite](https://shop.luxonis.com/collections/oak-cameras-1/products/oak-1-lite?variant=42583148069087) camera running MobileNet SSD for detecting and tracking people and their dogs. Since the resolution isn't that great, these images are forwarded via ethernet cable to the second part of caNNine, which is a finetuned ResNet-18 model running locally that can much more reliably categorize the dog as one worth dropping everything and going outside to pet, or not. The Luxonis camera is powered by a Raspberry Pi 4 (which in turn is powered by power over ethernet). Two small Flask apps handle requests and data transfer between the Raspberry Pi and the main computer.