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MitoEM-R-BC.yaml

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+MODEL:
+  OUT_PLANES: 2
+  TARGET_OPT: ["0", "4-1-1"]
+  LOSS_OPTION:
+    - - WeightedBCEWithLogitsLoss
+      - DiceLoss
+    - - WeightedBCEWithLogitsLoss
+      - DiceLoss
+  LOSS_WEIGHT: [[1.0, 0.5], [1.0, 0.5]]
+  WEIGHT_OPT: [["1", "0"], ["1", "0"]]
+  OUTPUT_ACT: [["none", "sigmoid"], ["none", "sigmoid"]]
+INFERENCE:
+  OUTPUT_ACT: ["sigmoid", "sigmoid"]
+  OUTPUT_PATH: outputs/MitoEM_R_BC/test/
+DATASET:
+  OUTPUT_PATH: outputs/MitoEM_R_BC/

MitoEM-R-Base.yaml

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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