license: mit
library_name: ultralytics
pipeline_tag: object-detection
base_model: Ultralytics/YOLO11
base_model_relation: finetune
language:
- en
tags:
- ultralytics
- yolo
- yolo11
- yolo11n
- yolov11
- yolov11n
- object-detection
- computer-vision
- waste-detection
- trash-detection
- garbage-detection
- recycling
- recycling-automation
- waste-sorting
- edge-ai
- onnx
- tflite
datasets:
- dmedhi/garbage-image-classification-detection
- garythung/trashnet
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:
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.