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
File size: 661 Bytes
8acec67 f6a8e6d 8acec67 f6a8e6d 8acec67 f6a8e6d a026e8c f6a8e6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # 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
```
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*Generated by MIRA — Machine Intelligence for Recycling Automation* |