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 | |
| ```python | |
| from ultralytics import YOLO | |
| model = YOLO("mira_exp019.pt") | |
| results = model.val(data='dataset.yaml') | |
| ``` | |
| ## Export | |
| ```bash | |
| yolo export model=best.pt format=tflite int8=True | |
| yolo export model=best.pt format=onnx | |
| ``` | |
| --- | |
| *Generated by MIRA — Machine Intelligence for Recycling Automation* |