Object Detection
ultralytics
LiteRT
Keras
ONNX
English
yolo
yolo11
yolo11n
yolov11
yolov11n
computer-vision
waste-detection
trash-detection
garbage-detection
recycling
recycling-automation
waste-sorting
edge-ai
Instructions to use Jeremy341/MIRA-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Jeremy341/MIRA-AI with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("Jeremy341/MIRA-AI", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 771 Bytes
743d863 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Third-party model descriptor for MIRA benchmarking.
#
# To benchmark a third-party model:
# 1. Place the model file in models/detection/
# 2. Create a YAML descriptor like this one
# 3. Run: mira benchmark --models <model_name> --dataset datasets/mira_all
#
# Supported types: yolo_pt, yolo_tflite, tflite, onnx, keras
#
# Ultralytics-compatible models (.pt, .tflite) work out of the box —
# the adapter will load them via `ultralytics.YOLO()` automatically.
# Non-ultralytics models (e.g. raw keras/tf SavedModel) need a custom
# adapter subclass that overrides load() and predict() in models.py.
name: "Example Third-Party Model"
type: tflite
model_file: example_third_party.tflite
imgsz: 320
class_names: [glass, metal, paper, plastic, trash]
preprocessing: null
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