As we all know, plastic pollution is a severe problem to our ecosystem. In respond to this problem, our team had created a plastic detecting model. In this Github Repository, you will know how to use the model, and train the model with your own dataset.
- Local Linux environment or Google Colab Notebook
- Detectron2
- Jupyter Notebook
- pytorch 1.8
- torchvision
- OpenCV
- Numpy
You can either train using our Colab Notebook or in a local linux enviornment.
Clone the repository and open colab.ipynb
for model training and usage. Details of using the codes will be included in the notebooks.
We've trained our photos with different backbones and sample size. Backbones can be found in detectron2/MODEL_ZOO. The samples are selected randomly from the training dataset. The configurations used for all training are as follow:
- Backbone used: X101-FPN3x, R101-FPN3x, R50-FPN3x
- Batch size per step: 2
- Iterations: 1000
- Train image percentages used: 100%, 75%, 50%, 25% of training data
Backbone | AP | AP50 | AP75 | APs | APm | APl | model |
---|---|---|---|---|---|---|---|
R50-FPN3x | 51.1 | 72.2 | 60.6 | 16.7 | 44.8 | 68.3 | model |
R101-FPN3x | 52.3 | 74.8 | 59.9 | 19.0 | 45.7 | 69.0 | model |
X101-FPN3x | 54.8 | 77.8 | 61.4 | 10.9 | 48.8 | 72.5 | model |
(Models here are all using R50-FPN backbone)
Train Images Percentage | AP | AP50 | AP75 | APs | APm | APl |
---|---|---|---|---|---|---|
100 | 51.1 | 72.2 | 60.6 | 16.7 | 44.8 | 68.3 |
75 | 49.6 | 70.0 | 58.2 | 12.0 | 44.0 | 66.7 |
50 | 48.6 | 70.4 | 58.0 | 12.9 | 43.0 | 64.0 |
25 | 48.0 | 68.4 | 57.5 | 11.9 | 43.4 | 63.0 |