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import os | |
import torch | |
MAIN_DIR = "/kaggle/working/" | |
# DATASET = os.path.join(MAIN_DIR, "../data/PASCAL_VOC") | |
DATASET = "/kaggle/input/pascal-voc-dataset-used-in-yolov3-video/PASCAL_VOC" | |
DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
# DEVICE = "mps" | |
# seed_everything() # If you want deterministic behavior | |
NUM_WORKERS = 2 | |
BATCH_SIZE = 40 | |
IMAGE_SIZE = 416 | |
INPUT_RESOLUTIONS = [416, 544] | |
INPUT_RESOLUTIONS_CUM_PROBS = [50, 100] | |
NUM_CLASSES = 20 | |
LEARNING_RATE = 1e-5 | |
WEIGHT_DECAY = 1e-4 | |
NUM_EPOCHS = 40 | |
CONF_THRESHOLD = 0.05 | |
MAP_IOU_THRESH = 0.5 | |
NMS_IOU_THRESH = 0.45 | |
S = [IMAGE_SIZE // 32, IMAGE_SIZE // 16, IMAGE_SIZE // 8] | |
PIN_MEMORY = True | |
LOAD_MODEL = False | |
SAVE_MODEL = True | |
CHECKPOINT_PATH = os.path.join(MAIN_DIR, "Store/checkpoints/") | |
IMG_DIR = DATASET + "/images/" | |
LABEL_DIR = DATASET + "/labels/" | |
TRAIN_MOSAIC_PERCENTAGE = 0.5 | |
TEST_MOSAIC_PERCENTAGE = 0.00 | |
MODEL_STATE_DICT_PATH = os.path.join(MAIN_DIR, "Store/checkpoints/yolov3.pth") | |
ANCHORS = [ | |
[(0.28, 0.22), (0.38, 0.48), (0.9, 0.78)], | |
[(0.07, 0.15), (0.15, 0.11), (0.14, 0.29)], | |
[(0.02, 0.03), (0.04, 0.07), (0.08, 0.06)], | |
] # Note these have been rescaled to be between [0, 1] | |
means = [0.485, 0.456, 0.406] | |
scale = 1.1 | |
PASCAL_CLASSES = [ | |
"aeroplane", | |
"bicycle", | |
"bird", | |
"boat", | |
"bottle", | |
"bus", | |
"car", | |
"cat", | |
"chair", | |
"cow", | |
"diningtable", | |
"dog", | |
"horse", | |
"motorbike", | |
"person", | |
"pottedplant", | |
"sheep", | |
"sofa", | |
"train", | |
"tvmonitor", | |
] | |
COCO_LABELS = [ | |
"person", | |
"bicycle", | |
"car", | |
"motorcycle", | |
"airplane", | |
"bus", | |
"train", | |
"truck", | |
"boat", | |
"traffic light", | |
"fire hydrant", | |
"stop sign", | |
"parking meter", | |
"bench", | |
"bird", | |
"cat", | |
"dog", | |
"horse", | |
"sheep", | |
"cow", | |
"elephant", | |
"bear", | |
"zebra", | |
"giraffe", | |
"backpack", | |
"umbrella", | |
"handbag", | |
"tie", | |
"suitcase", | |
"frisbee", | |
"skis", | |
"snowboard", | |
"sports ball", | |
"kite", | |
"baseball bat", | |
"baseball glove", | |
"skateboard", | |
"surfboard", | |
"tennis racket", | |
"bottle", | |
"wine glass", | |
"cup", | |
"fork", | |
"knife", | |
"spoon", | |
"bowl", | |
"banana", | |
"apple", | |
"sandwich", | |
"orange", | |
"broccoli", | |
"carrot", | |
"hot dog", | |
"pizza", | |
"donut", | |
"cake", | |
"chair", | |
"couch", | |
"potted plant", | |
"bed", | |
"dining table", | |
"toilet", | |
"tv", | |
"laptop", | |
"mouse", | |
"remote", | |
"keyboard", | |
"cell phone", | |
"microwave", | |
"oven", | |
"toaster", | |
"sink", | |
"refrigerator", | |
"book", | |
"clock", | |
"vase", | |
"scissors", | |
"teddy bear", | |
"hair drier", | |
"toothbrush", | |
] |