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.

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Training Data

Datasets

1. Classifcation waste Computer Vision Model by GKHANG

   Classes: 10

   Images: 10,289

2. Trash Computer Vision Dataset by BAILE

   Classes: 48

   Images: 101

Class Distribution

After merging the two datasets, I reorganized the classes into their new perspective classes seen in the table below.

Recycle - 6,640 - 5,033 = 1,607	Trash - 1,023	Compost - 1,814	Specialized Disposal - 1,026	Deleted - 27
Glass Paper Cardboard Metal Plastic Glass Drink can Pop tab Clear plastic bottle Food can Glass bottle Glass jar Other plastic bottle Normal paper Other carton Other plastic wrapper Aerosol Aluminium foil Drink carton Paper bag Toilet tube Corrugated carton Metal lid Spread tub Meal carton	Broken glass Gloves Masks Cigarette Plastic film Foam cup Disposable food container Crisp packet Metal bottle cap Plastic lid Plastic straws Plastic utensils Paper cup Aluminium blister pack Garbage bag Tissues Styrofoam piece Paper straw Single use carrier bag Squeezable tube Rope & string Shoe 6 pack rings Disposable plastic cup	Biodegradable Food waste Egg carton	Syringe Medicines Metal bottle cap Battery	Plastic bottle caps Glass cup Unlabeled litter Other plastic

This is the final class distributions

Class	Train	Valid	Test	Total
Recycle	992	324	234	1,607
Trash	626	223	151	1,023
Compost	1,151	389	269	1,814
Specialized Disposal	667	219	140	1,026

Annotation Process

For the compost class, some images included items that could not be composted (ex, red meat). I reviewed all the images and moved non-compostable food waste to the trash category.

For the classes I chose to delete, the first one being plastic bottle caps, from Google searches, it is generally recommended to keep your bottle caps on your bottles when recycling, but for recycling plastic bottle caps, there were specific requirements on what size can and cannot be recycled. For example, according to the Seattle Public Utilities, loose bottle caps less than 3 inches in diameter go into the trash (Seattle Public Utilities). However, from the images alone, it’s difficult to interpret the size of the caps, so for less confusion in training, I choose to opt out of including them. For glass cups, they cannot be recycled and generally recommended to donate them; however, since there are only 3 images in this class, rather than adding a new “donate” class and risk significant class imbalance, I choose to delete them. Lastly, for both unlabelled litter and other plastic, it was difficult to identify these items, so I chose to delete them to minimize confusion.

For the recycle class, it came up to a total of 6,640 images, but because the rest of the classes were within the 1,000 range, and I wanted to try to prevent any false negatives and accuracy issues from imbalanced classes, I chose to delete 5,033 images from the class and ended up with 1,607.

Train/Valid/Test Split

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

Augmentations

• None

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Training Procedure

• Framework: Ultralytics

• Hardware: NVIDIA A100-SXM4-80GB

• Batch Size: 64

• Epochs: 50

• Image Size: 640

• Patience: 10

• Preprocessing: None

• Early Stopping: 38 epochs

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Evaluation Results

Overall Breakdown

Top 1 Accuracy	F1-Score	Precision	Recall
0.962	0.96	0.96	0.96

Per-Class Breakdown

Class	Precision	Recall	F1-Score
Recycle	0.98	0.96	0.97
Trash	0.95	0.91	0.93
Compost	0.98	0.99	0.98
Specialized Disposal	0.98	0.99	0.98

Confusion Matrix

Train/Loss and Val/Loss Curves

Peformance Analysis

From this model, the overall performance indicate high accuracy, with the top 1 accuracy being 0.962 and each class having a F1-score in the 0.9 range. From the confusion matrix, we can see a perfect diagonal, which indicates the model was able to accurately predict the items correctly. However we do see a few false negatives and positives, specifically where the model mixed up trash and specalized disposal is the highest, but this is very minor. Looking at the train/loss curve shows that the model is learning effectively, with the downward shape curve and decreasing spikes as the longer the model trains. From the metrics/accuracy_top1 curve we see large spikes towards the beginning of the training process but the spikes slowly decreases as it trains longer, which indicates the model is improving it’s accuracy and showing stable performance. For the validation loss curve, we see extremely high spikes towards the beginning, but the spikes slowly decreases as more training time is applied. Lastly, for the metrics/accuracy_top5, this shows up as a horizontal line at 1 due to the fact that I only have 4 classes. Overall, my model indicates high accuracy and no cases of overfitting, however, the model could benefit from longer training time to allow the curves to smoothen out further and reach an eventual straight horizontal line.

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Limitations and Biases

Failure Cases

• Struggled at identifying compost majority of the time
• Confused specialized disposal for trash

Limitation

• Decisions on which classes belong in which were made based on Seattle's disposal guidelines, which can’t be used worldwide or statewide due to different disposal requirements and regulations among different areas.

Poor Performing Class: Compost

• Majority of images in the compost class feature pixelated or low quality images, this makes it difficult for the model to identify.