# CircularNet

Instance segmentation models for identification of recyclables on conveyor
belts.

Note that these are demo models, reach out to
waste-innovation-external@google.com for updated versions of the models.

## Overview

Mask RCNN is a state-of-art deep learning model for instance image segmentation,
where the goal is to assign instance level labels ( e.g. person1, person2, cat)
to every pixel in an input image. Mask RCNN algorithm is available in the
TensorFlow Model Garden which is a repository with a number of different
implementations of state-of-the-art models and modeling solutions for TensorFlow
users.

## Model Categories

-   Material model - Detects the high level category (e.g. plastic, paper, etc)
    of an object according to its material type.
-   Material Form model - Detects the category of the of an object according to
    its physical product formation (e.g. cup, plate, pen, etc).
-   Plastic model - Detects the category of a object according to its plastic
    types (e.g. HDPE, LDPE, etc)

> The goal to develop these models is to bring transparency & traceability in
the world of  waste recycling.

## Model paths in GCP buckets

| Model categories | Model backbone | Model type | GCP bucket path |
| ------ | ------ | ----- | ------ |
| Material Model | Resnet | saved model & TFLite | [click here](https://storage.googleapis.com/tf_model_garden/vision/waste_identification_ml/material_model.zip) |
| Material Form model | Resnet | saved model & TFLite | [click here](https://storage.googleapis.com/tf_model_garden/vision/waste_identification_ml/material_form_model.zip) |
|Plastic model | Resnet| saved model & TFLite | [click here](https://storage.googleapis.com/tf_model_garden/vision/waste_identification_ml/plastic_types_model.zip) |
