Trash Classification

The repo contains code for a SSD-Sinlge Shot Detector to detect trash in an image. It takes input as an image and outputs bounding boxes over all the instance of trash in the image.

Motivation

This project was done as a part of the CS344/CS386 Course Project, under the guidance of Dr. Clint P. George.

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Tech

Built with

• Python

Sources/Refrences

• The repository https://github.com/pierluigiferrari/ssd_keras is a main source of code.
• The SSD paper : https://arxiv.org/pdf/1512.02325.pdf
• The dataset is from : https://github.com/pedropro/TACO.

For details on the dataset : http://tacodataset.org/

Libraries

The libraries used in building the project are listed here :

• Tensorflow 2.x
• Keras 2.x
• sklearn, imageio,open-cv

Usage

Clone the repo and then in the TACO/Data directory run the following command: All dependencies are provided in the requirement.txt file

python3 download.py

This will download the TACO dataset for trash detection into the proper directory sa required to run other code.

From the SSD folder run the following commands depending on usage: Two models are present in the model_weights folder Model: Uses mapping of 10 categories Model_v2: Uses mapping of 19 categories Train:

python3 run.py train --dataset=../TACO/data --annot_train=map10_without_batch10_15_train.json --annot_val=map10_without_batch10_15_val.json

python3 run.py train --dataset=../TACO/data --config=config_v2.json --model=model_weights/Model_v2.h5 --annot_train=custom_map_train_lskw_rm10_15.json --annot_val=custom_map_val_lskw.json

Evaluate: In this just give a single image path

python3 run.py evaluate --evaluate_img=../TACO/data/batch_12/000044.jpg   --model=model_weights/Model.h5 --config=config.json

python3 run.py evaluate --evaluate_img=../TACO/data/batch_12/000044.jpg --model=model_weights/Model_v2.h5 --config=config_v2.json

Test image paths are provided in test_samples.txt change the path of image in the above command with the image path to generate the images.

The config files contain all the required parameters as descirbed in config_details.csv

The model sets provided are :

For Model.h5 and config.json

• SSD/map10_without_batch10_15_train.json
• SSD/map10_without_batch10_15_val.json

For Model_v2.h5 and config_v2.json

• SSD/custom_map_train_lskw_rm10_15.json
• SSD/custom_map_val_lskw.json

For details on running the run.py with different arguments do

python3 run.py -h

Team

Ankit, Devyani, Siddharth (Indian Institute of Technology Goa)