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Jeremy341/MIRA-AI

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Model Card

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:

bash

pip install ultralytics huggingface_hub

Download the recommended model directly from Hugging Face:

python

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:

python

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.