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: 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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