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f6ab35f | 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | from __future__ import annotations
from pathlib import Path
APP_ROOT = Path(__file__).resolve().parents[1]
MODELS_DIR = APP_ROOT / "models"
RESULTS_DIR = APP_ROOT / "results"
SELECTED_ENSEMBLE_PATH = RESULTS_DIR / "selected_ensemble.json"
CLASS_NAMES = ["glioma", "meningioma", "notumor", "pituitary"]
CLASS_DISPLAY_NAMES = {
"glioma": "Glioma",
"meningioma": "Meningioma",
"notumor": "No tumor",
"pituitary": "Pituitary",
}
IMAGE_SIZE = 224
NORMALIZE_MEAN = [0.485, 0.456, 0.406]
NORMALIZE_STD = [0.229, 0.224, 0.225]
# The selected deployment ensemble from the ablation notebook.
# Zero-weight members are intentionally omitted.
ENSEMBLE_MEMBERS = [
{
"member": "efficientnet_b0__seed123",
"model_name": "efficientnet_b0",
"seed": 123,
"weight": 0.49513684,
"checkpoint_file": "best_efficientnet_b0_seed123.pt",
"display_name": "EfficientNet-B0 路 seed 123",
},
{
"member": "efficientnet_b0__seed2026",
"model_name": "efficientnet_b0",
"seed": 2026,
"weight": 0.35077890,
"checkpoint_file": "best_efficientnet_b0_seed2026.pt",
"display_name": "EfficientNet-B0 路 seed 2026",
},
{
"member": "mobilenet_v3_small__seed42",
"model_name": "mobilenet_v3_small",
"seed": 42,
"weight": 0.15408426,
"checkpoint_file": "best_mobilenet_v3_small_seed42.pt",
"display_name": "MobileNetV3-Small 路 seed 42",
},
]
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