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c227be5 | 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 | from dotenv import load_dotenv
import os
load_dotenv()
os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
# ── Camera & optics (single source of truth) ───────────────────────────────────
SPECS = {
"s2": {"pixel_m": 7.5e-6, "focal_m": 0.600, "alt_m": 786_000.0},
"bc2": {"pixel_m": 6.0e-6, "focal_m": 0.016, "alt_m": 410_000.0,
"f_number": 2.8},
}
def gsd(cam):
return cam["pixel_m"] * cam["alt_m"] / cam["focal_m"]
GSD_S2 = gsd(SPECS["s2"]) # 9.83 m
GSD_BC2 = gsd(SPECS["bc2"]) # 153.75 m
SCALE_FACTOR = GSD_BC2 / GSD_S2 # ~15.65 (replaces old altitude-only ratio)
# ── Camera sensor dimensions ───────────────────────────────────────────────────
CAMERA_W = 752 # physical BlueFOX sensor width (inference frame size)
CAMERA_H = 480 # physical BlueFOX sensor height (inference frame size)
PATCH_SIZE = 33 # output size of each training patch after GSD downsampling
# = floor(512 / SCALE_FACTOR)
# ── Paths ──────────────────────────────────────────────────────────────────────
SAVE_DIR = "data/bluefox"
CHECKPOINT = "best_model.pth"
# ── Dataset ────────────────────────────────────────────────────────────────────
MAX_SAMPLES = 8490
SEED = 42
TRAIN_RATIO = 0.70
VAL_RATIO = 0.15
TEST_RATIO = 0.15
# ── Model ──────────────────────────────────────────────────────────────────────
NUM_CLASSES = 4
CLASS_NAMES = ["clear", "thick_cloud", "thin_cloud", "shadow"]
# ── Training ───────────────────────────────────────────────────────────────────
BATCH_SIZE = 512 # 33×33 patches are tiny -- can afford larger batches
NUM_WORKERS = 0
NUM_EPOCHS = 50
PATIENCE = 8
LEARNING_RATE = 2e-4 # bumped up slightly from 2e-5 -- fine for a small CNN
WEIGHT_DECAY = 0.01 |