cvtechniques/Waste-Identifier

Waste Identifer Classifcation Model

By Amanda Sim

Context

This classification model aims to identify items and categorize them based on how they should be disposed of. Using YOLOv11, this model fine-tunes previously trained datasets from Roboflow to fit new classes: recycle, trash, compost, and specialized disposal. This model is intented to be used to help people correctly dispose of their items and can be used for smart bins, which detected the item a person is holding and opens to the appropriate bin or for apps where the user can take a photo of the item and identify where it goes and how to dispose of it

Training Data

Datasets

1. Classifcation waste Computer Vision Model by GKHANG

   Classes: 10

   Images: 10,289

   Link: https://universe.roboflow.com/gkhang/classification-waste

2. Trash Computer Vision Dataset by BAILE

   Classes: 48

   Images: 101

   Link: https://universe.roboflow.com/baile/trash-izcuy

Class Distribution

Class	Count
Recycle	1,607
Trash	1,023
Compost	1,814
Specialized Disposal	1,026

Annotation Process

Train/Valid/Test Split

• Train: 3,421 images (64%)
• Valid: 1,145 images (21%)
• Test: 791 images (15%)

Augmentations

• None

Training Procedure

• Framework: Ultralytics

• Hardware: NVIDIA A100-SXM4-80GB

• Batch Size: 64

• Epochs: 50

• Image Size: 640

• Patience: 10

• Early Stopping: 38 epochs

Evaluation Results

Limitations and Biases