Jeremy341/MIRA-AI

MIRA - YOLO11n Waste Detection for Recycling and Waste Sorting

MIRA is a custom YOLO11n object-detection project for waste detection, recycling automation, and automated waste sorting.

The models detect five classes:

• glass
• metal
• paper
• plastic
• trash

This repository contains PyTorch, ONNX, and TFLite exports from the MIRA experiments. The recommended reference model is mira_exp019.pt.

• GitHub: https://github.com/jeremy341/MIRA-AI
• Project website: https://mira-vision.vercel.app/
• PyPI package: https://pypi.org/project/mira-ai/

EXP-019 performance

Metric	Result
mAP50	90.58%
mAP50-95	82.15%
Precision	87.2%
Recall	84.6%
Training images	5,108
Validation images	415
Test images	1,375
Classes	5

These results come from the documented evaluation split used for EXP-019.

Available model files

File	Format	Description
mira_exp019.pt	PyTorch	Recommended YOLO11n detector
mira_exp019.onnx	ONNX	ONNX export of EXP-019
mira_exp019_int8_320.tflite	TFLite	INT8 export at 320 px
mira_exp019_int8_640.tflite	TFLite	INT8 export at 640 px

The repository also contains models from earlier MIRA experiments.

Experiment results

Experiment	Model	Dataset	mAP50
EXP-005	YOLOv8n	Custom + TrashNet	82.3%
EXP-006	YOLOv8n	Fused Wild + TrashNet	39.4%
EXP-009	YOLOv8n	TrashNet	72.8%
EXP-011	YOLOv8n	TACO	35.0%
EXP-013	YOLO11n	TACO + TrashNet	55.1%
EXP-014	YOLO11n	Combined dataset	60.7%
EXP-015	YOLO11n	Combined dataset with WaRP	56.0%
EXP-016	YOLO11n	WaRP-focused dataset	58.8%
EXP-017	YOLO11n	Larger combined dataset	59.3%
EXP-018	YOLO11n	Clean balanced dataset	90.6%
EXP-019	YOLO11n	Clean balanced repeatability run	90.58%

The main lesson was that adding more data did not automatically improve the model. Removing inconsistent examples and building a cleaner, more balanced dataset led to the strongest results in EXP-018 and EXP-019.

Quick start

Install the required packages:

pip install ultralytics huggingface_hub

Download the recommended model directly from Hugging Face:

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

model_path = hf_hub_download(
    repo_id="Jeremy341/MIRA-AI",
    filename="mira_exp019.pt",
)

model = YOLO(model_path)

results = model.predict(
    "image.jpg",
    conf=0.25,
    save=True,
)

results[0].show()

For validation, provide a compatible YOLO dataset configuration:

results = model.val(data="dataset.yaml")

Intended use

MIRA is intended for research and prototyping in:

• waste detection
• recycling automation
• waste sorting
• computer-vision research
• edge-AI object detection
• robotic sorting experiments

The models are not presented as a finished production recycling system.

Datasets

The models were trained using combinations of:

• dmedhi garbage image classification/detection
• TACO
• TrashNet
• Roboflow Trash Detection

The datasets were remapped to the five MIRA classes. Each dataset remains subject to its original license and usage terms.

Limitations

The models can struggle with:

• white crumpled paper
• cans viewed from the opening
• strongly overlapping objects
• unusual lighting
• unusual viewing angles
• waste objects outside the training distribution

The reported results do not guarantee the same performance on completely independent real-world images.

License

The model files are provided under the MIT License where applicable. Dataset licenses remain subject to their original terms.