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MIRA-AI

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  1. .gitattributes +3 -0
  2. models/classifier/mira_classifier_baseline.keras +3 -0
  3. models/classifier/mira_classifier_fp32.tflite +3 -0
  4. models/classifier/mira_classifier_int8.tflite +3 -0
  5. models/classifier/mira_classifier_transfer.keras +3 -0
  6. models/classifier/mira_classifier_tuned.keras +3 -0
  7. models/detection/example_third_party.yaml +20 -0
  8. models/detection/gianlucasposito_yolov8n.pt +3 -0
  9. models/detection/gianlucasposito_yolov8n.yaml +13 -0
  10. models/detection/mira_exp006.pt +3 -0
  11. models/detection/mira_exp006_int8.tflite +3 -0
  12. models/detection/mira_exp009_int8.tflite +3 -0
  13. models/detection/mira_exp011.pt +3 -0
  14. models/detection/mira_exp011_int8.tflite +3 -0
  15. models/detection/mira_exp013.pt +3 -0
  16. models/detection/mira_exp013_int8.tflite +3 -0
  17. models/detection/mira_exp014.pt +3 -0
  18. models/detection/mira_exp014.yaml +14 -0
  19. models/detection/mira_exp014_int8.tflite +3 -0
  20. models/detection/mira_exp015.pt +3 -0
  21. models/detection/mira_exp015_int8.tflite +3 -0
  22. models/detection/mira_exp016.pt +3 -0
  23. models/detection/mira_exp016_int8.tflite +3 -0
  24. models/detection/mira_exp017.onnx +3 -0
  25. models/detection/mira_exp017.pt +3 -0
  26. models/detection/mira_exp017.yaml +19 -0
  27. models/detection/mira_exp017_int8.tflite +3 -0
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+ # Third-party model descriptor for MIRA benchmarking.
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+ #
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+ # To benchmark a third-party model:
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+ # 1. Place the model file in models/detection/
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+ # 2. Create a YAML descriptor like this one
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+ # 3. Run: mira benchmark --models <model_name> --dataset datasets/mira_all
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+ #
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+ # Supported types: yolo_pt, yolo_tflite, tflite, onnx, keras
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+ #
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+ # Ultralytics-compatible models (.pt, .tflite) work out of the box —
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+ # the adapter will load them via `ultralytics.YOLO()` automatically.
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+ # Non-ultralytics models (e.g. raw keras/tf SavedModel) need a custom
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+ # adapter subclass that overrides load() and predict() in models.py.
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+
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+ name: "Example Third-Party Model"
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+ type: tflite
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+ model_file: example_third_party.tflite
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+ imgsz: 320
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+ class_names: [glass, metal, paper, plastic, trash]
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+ preprocessing: null
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+ name: gianlucasposito_yolov8n
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+ display_name: \"gianlucasposito YOLOv8n (5-class waste)\"
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+ model_type: third_party
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+ task: detect
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+ input_size: 640
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+ class_names: [\"glass\", \"metal\", \"paper\", \"plastic\", \"trash\"]
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+ model_file: gianlucasposito_yolov8n.pt
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+ training:
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+ dataset: \"TACO subset\"
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+ framework: \"YOLOv8n\"
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+ tags: [\"third-party\", \"yolov8n\", \"taco\", \"baseline\"]
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+ recommended_for: \"benchmarking\"
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+ source_url: \"https://github.com/gianlucasposito/YOLO-Waste-Detection\"
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+ name: mira_exp014
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+ display_name: \"EXP-014 (YOLO11n, TACO+TrashNet+Roboflow)\"
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+ model_type: yolo_pt
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+ task: detect
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+ input_size: 640
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+ class_names: [\"glass\", \"metal\", \"paper\", \"plastic\", \"trash\"]
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+ training:
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+ dataset: \"TACO + TrashNet + Roboflow\"
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+ epochs: 120
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+ metrics:
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+ map50: 0.607
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+ map50_95: 0.455
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+ tags: [\"yolo11n\", \"multidataset\", \"best-so-far\"]
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+ recommended_for: \"raspberry_pi\"
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+ name: mira_exp017
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+ display_name: \"EXP-017 (YOLO11n, all 4 sources)\"
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+ model_type: yolo_pt
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+ task: detect
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+ input_size: 640
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+ class_names: [\"glass\", \"metal\", \"paper\", \"plastic\", \"trash\"]
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+ training:
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+ dataset: \"TACO + TrashNet + Roboflow + WaRP\"
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+ epochs: 120
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+ batch_size: 32
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+ imgsz: 640
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+ device: \"Tesla T4\"
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+ metrics:
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+ map50: 0.593
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+ map50_95: 0.465
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+ precision: 0.639
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+ recall: 0.549
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+ tags: [\"yolo11n\", \"multidataset\", \"all-4-sources\"]
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+ recommended_for: \"raspberry_pi\"
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