| --- |
| 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: |
|
|
| ```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](https://huggingface.co/datasets/dmedhi/garbage-image-classification-detection) |
| - [TACO](https://github.com/pedropro/TACO) |
| - [TrashNet](https://github.com/garythung/trashnet) |
| - [Roboflow Trash Detection](https://universe.roboflow.com/jerry-jukbu/trash-detection-1fjjc-uqlv1) |
|
|
| 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. |
|
|
