Save the world by recycling waste. Waste Management and Segregation are essential to every household. I have built a system that uses the embedded vision starter kit along with the Basler camera & UP board to segregate your everyday trash into categories of paper, plastic, glass, metal and others.
Being able to sort trash according to different materials is very important for recycling. However, sorting trash is one of the toughest tasks to do. While it is easy to sort metals and non-metals, it is very difficult to sort paper, glass, plastic, and cardboard.
Currently, it is done by people. It is not a good job and such people are often in danger of being exposed to harmful chemicals, medical wastes and be exposed to diseases. If instead, we can use a neural network that can do the classification then we can make the process faster, safer and more accurate.
This project attempts to use a convolutional neural network to do just that.
It is not always possible to run a machine learning model on a GPU as there can be cost and space restrictions. And always making an API call can have latencies and internet might not always be available.
In these cases using small, cheap devices at the edge (where the data is generated) is the best solution.
The problem with running models on the edge is that we are limited by the amount of computation power that we have. There are many ways to overcome this. You could use a hardware accelerator like a Neural Computer Stick. Or you could use some models that are built specifically to not be computationally expensive and run on the edge.
In this project, we use the UP Board Embedded Vision Kit to run inference on the edge. We use the MobileNet model which is computationally less expensive.
The whole project is available here
The data for this project was collected from the trashnet project
Sample code for training
python3 mobilenet_training.py --nb_epoch 2 --batch_size 32 --model models/model1.h5
Sample code for classification.
Note: This will run only with the basler camera.
This project requires python3.6 and opencv. Other requirements are present in the requirements.txt file.
|soham96||pushed 6dda8c1ce56f750585d52818858efc38aebda182||2019-01-04 12:13:13 UTC|
|Soham Chatterjee||pushed 91d687f0293725cbe0536a3c6b574bddb9000cba||2018-11-30 06:24:48 UTC|
|Soham Chatterjee||pushed 2eab573aea1151d4e97080287a3bc27fa4f77798||2018-11-30 06:24:33 UTC|
|Soham Chatterjee||pushed 3bd0cb2ac59b9ec84bebbeb75cc3dacdf509f47b||2018-11-29 17:46:58 UTC|
|Soham Chatterjee||pushed 70265ac98f4214adecdd25b3627e2ea71e52edfe||2018-11-29 17:16:26 UTC|
|Soham Chatterjee||pushed 0c7663569f2ca323b3269ecec44168c624f65a57||2018-11-29 17:13:17 UTC|
|ArchanaIyer1996||pushed 22c51773c8471c093609f3565c5e427c61862549||2018-11-29 14:10:37 UTC|
|96soham96||pushed 14cdf88de112796342b11b30ae96468fe0075acc||2018-11-29 06:18:34 UTC|
|96soham96||pushed 057e01bb3810907bcb2b172d57b13c43a40fff93||2018-11-28 14:36:43 UTC|
|ArchanaIyer1996||pushed a2e6cac9936a35786630b720e703686c241f89c5||2018-11-28 13:25:59 UTC|
|96soham96||pushed 0605ca055ed498dc69fb8c40deab99a37d7f81d6||2018-11-28 12:39:13 UTC|
|soham96||pushed a12e1cd4d85fe3be600c05dc76361aa1bb3079eb||2018-11-24 18:13:14 UTC|
|Soham Chatterjee||pushed eb37790f7c276796c0beab347bcc08af1050783d||2018-11-24 11:15:14 UTC|
|Prediction Image for Paper||png|