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fast-reid/projects/FastClas/configs/base-clas.yaml

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update fastreid V1.0
2021-01-18 11:36:38 +08:00
MODEL:
META_ARCHITECTURE: Baseline
BACKBONE:
NAME: build_resnet_backbone
DEPTH: 50x
NORM: BN
LAST_STRIDE: 2
FEAT_DIM: 2048
PRETRAIN: True
HEADS:
support classification in fastreid (#443) Summary: support classification and refactor build_dataloader which can support explicit parameters passing
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NAME: ClasHead
update fastreid V1.0
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WITH_BNNECK: False
EMBEDDING_DIM: 0
support classification in fastreid (#443) Summary: support classification and refactor build_dataloader which can support explicit parameters passing
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POOL_LAYER: FastGlobalAvgPool
CLS_LAYER: Linear
NUM_CLASSES: 2
update fastreid V1.0
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LOSSES:
NAME: ("CrossEntropyLoss",)
CE:
EPSILON: 0.1
SCALE: 1.
INPUT:
support classification in fastreid (#443) Summary: support classification and refactor build_dataloader which can support explicit parameters passing
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SIZE_TEST: [256, 256]
CROP:
ENABLED: True
SIZE: [224, 224]
FLIP:
ENABLED: True
AUGMIX:
ENABLED: False
AUTOAUG:
ENABLED: False
PADDING:
ENABLED: False
update fastreid V1.0
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CJ:
ENABLED: False
support classification in fastreid (#443) Summary: support classification and refactor build_dataloader which can support explicit parameters passing
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AFFINE:
ENABLED: False
update fastreid V1.0
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REA:
ENABLED: False
DATALOADER:
support classification in fastreid (#443) Summary: support classification and refactor build_dataloader which can support explicit parameters passing
2021-03-26 20:17:39 +08:00
SAMPLER_TRAIN: TrainingSampler
update fastreid V1.0
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NUM_WORKERS: 8
SOLVER:
MAX_EPOCH: 100
support classification in fastreid (#443) Summary: support classification and refactor build_dataloader which can support explicit parameters passing
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AMP:
ENABLED: True
update fastreid V1.0
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OPT: SGD
SCHED: CosineAnnealingLR
BASE_LR: 0.003
MOMENTUM: 0.99
NESTEROV: True
BIAS_LR_FACTOR: 1.
WEIGHT_DECAY: 0.0005
WEIGHT_DECAY_BIAS: 0.
IMS_PER_BATCH: 128
ETA_MIN_LR: 0.00003
WARMUP_FACTOR: 0.1
make lr warmup by iter Summary: change warmup way by iter not by epoch, which will make it more flexible when training small epochs
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WARMUP_ITERS: 2000
update fastreid V1.0
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CHECKPOINT_PERIOD: 10
TEST:
EVAL_PERIOD: 10
IMS_PER_BATCH: 256
DATASETS:
NAMES: ("Hymenoptera",)
TESTS: ("Hymenoptera",)
save idx2class dict when training classification (#444)
2021-03-26 20:51:52 +08:00
OUTPUT_DIR: projects/FastClas/logs/baseline