Garbage image classification using PyTorch transfer learning pretrained model Resnet152 with FastAPI for API based application. This model will classification 6 class: cardboard, glass, metal, paper, plastic, trash (other).

$ git clone https://github.com/hafidh561/Garbage-Image-Classification.git

# Python version 3.6
$ git clone https://github.com/nodefluxio/vortex.git
$ cd vortex/ && git checkout drop-enforce
$ pip install ./src/runtime[onnxruntime] && cd ../
$ pip install -r requirements.txt
$ python download_model.py

# Newest docker version
$ docker build -t hafidh561/garbage-image-classification:1.0 .

$ python app.py

$ docker run --rm -p <YOUR PORT>:6969 hafidh561/garbage-image-classification:1.0
# Example
$ docker run --rm -p 301:6969 hafidh561/garbage-image-classification:1.0

1. After you run app use python or docker, open your web browser and go to http://localhost:<YOUR PORT | 6969>/docs for looking some documentation.

2. Now it's time to testing API, open your application for testing API. I'll use Postman for testing API.

3. Set up postman like this.

   

4. Press button "Select Files" to select image you want to classification.

5. Press "Send" button and waiting for response.

6. Now open response body and look object response member class and search for highest value.

{
	"filename": "glass.jpg",
	"contentype": "image/jpeg",
	"class": "glass",
	"confidence": "0.99995697"
}

• Indonesian Language

If you want make your own deep learning for image classification? Give it a try in this Google Colab

MIT LICENSE

© Developed by hafidh561 - Internship at Nodeflux