Instructions to use Jeremy341/MIRA-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Jeremy341/MIRA-AI with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Jeremy341/MIRA-AI") - Notebooks
- Google Colab
- Kaggle
MIRA — mira_exp019
Machine Intelligence for Recycling Automation — YOLO11 detection model.
Model Details
- Base model: YOLO11n
- Classes: glass, metal, paper, plastic, trash
- Task: Object detection for waste sorting
- Framework: Ultralytics YOLO
Additional Notes
YOLO11n repeatability run, clean balanced dataset
Usage
from ultralytics import YOLO
model = YOLO("mira_exp019.pt")
results = model.val(data='dataset.yaml')
Export
yolo export model=best.pt format=tflite int8=True
yolo export model=best.pt format=onnx
Generated by MIRA — Machine Intelligence for Recycling Automation