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SYSTEM:
NUM_GPUS: 1
NUM_CPUS: 1
MODEL:
ARCHITECTURE: unet_plus_3d
BLOCK_TYPE: residual_se
INPUT_SIZE: [17, 225, 225]
OUTPUT_SIZE: [17, 225, 225]
IN_PLANES: 1
NORM_MODE: sync_bn
FILTERS: [32, 64, 96, 128, 160]
DATASET:
IMAGE_NAME: ["im_train.json"]
LABEL_NAME: ["mito_train.json"]
INPUT_PATH: datasets/MitoEM_R/ # or your own dataset path
OUTPUT_PATH: outputs/MitoEM_R/
PAD_SIZE: [4, 64, 64]
DO_CHUNK_TITLE: 0
DATA_CHUNK_NUM: [4, 8, 8]
DATA_CHUNK_ITER: 10000
SOLVER:
LR_SCHEDULER_NAME: WarmupCosineLR
BASE_LR: 0.04
ITERATION_STEP: 1
ITERATION_SAVE: 5000
ITERATION_TOTAL: 150000
SAMPLES_PER_BATCH: 2
INFERENCE:
INPUT_SIZE: [17, 257, 257]
OUTPUT_SIZE: [17, 257, 257]
IMAGE_NAME: /n/holylfs05/LABS/pfister_lab/Lab/coxfs01/pfister_lab2/Lab/donglai/eng/db/eva/2000_73728-310272.h5
OUTPUT_PATH: outputs/MitoEM_R/test/
OUTPUT_NAME: result # will automatically save to HDF5
PAD_SIZE: [4, 64, 64]
AUG_MODE: mean
AUG_NUM: 4
STRIDE: [8, 128, 128]
SAMPLES_PER_BATCH: 8
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