We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
I successfully starting the training,but i meet some the problem which makes my accuracy very low. ''' Above is part of my training logs,obviously my training loss keeps very high. [05/11 11:05:31 d2.utils.events]: eta: 0:16:35 iter: 2839 total_loss: 13.81 loss_ce: 1.062 loss_bbox: 0.2362 loss_giou: 0.7347 loss_ce_0: 1.533 loss_bbox_0: 0.4347 loss_giou_0: 1.347 loss_ce_1: 1.076 loss_bbox_1: 0.3313 loss_giou_1: 0.9831 loss_ce_2: 0.983 loss_bbox_2: 0.2493 loss_giou_2: 0.8497 loss_ce_3: 1.095 loss_bbox_3: 0.2378 loss_giou_3: 0.8031 loss_ce_4: 1.037 loss_bbox_4: 0.2179 loss_giou_4: 0.7701 time: 0.4684 last_time: 0.4062 data_time: 0.0213 last_data_time: 0.0158 lr: 2.5e-07 max_mem: 4188M [05/11 11:05:41 d2.utils.events]: eta: 0:16:28 iter: 2859 total_loss: 15.24 loss_ce: 1.078 loss_bbox: 0.2586 loss_giou: 0.9109 loss_ce_0: 1.416 loss_bbox_0: 0.5326 loss_giou_0: 1.461 loss_ce_1: 1.054 loss_bbox_1: 0.3595 loss_giou_1: 1.137 loss_ce_2: 1.06 loss_bbox_2: 0.3113 loss_giou_2: 1.046 loss_ce_3: 1.061 loss_bbox_3: 0.2689 loss_giou_3: 0.9477 loss_ce_4: 1.049 loss_bbox_4: 0.249 loss_giou_4: 0.9952 time: 0.4684 last_time: 0.5241 data_time: 0.0168 last_data_time: 0.0031 lr: 2.5e-07 max_mem: 4188M [05/11 11:05:50 d2.utils.events]: eta: 0:16:21 iter: 2879 total_loss: 13.91 loss_ce: 0.9869 loss_bbox: 0.2141 loss_giou: 0.8199 loss_ce_0: 1.447 loss_bbox_0: 0.4575 loss_giou_0: 1.436 loss_ce_1: 1.119 loss_bbox_1: 0.3276 loss_giou_1: 1.095 loss_ce_2: 0.9777 loss_bbox_2: 0.257 loss_giou_2: 0.9116 loss_ce_3: 0.9843 loss_bbox_3: 0.2249 loss_giou_3: 0.8466 loss_ce_4: 0.9701 loss_bbox_4: 0.214 loss_giou_4: 0.8948 time: 0.4685 last_time: 0.4227 data_time: 0.0191 last_data_time: 0.0087 lr: 2.5e-07 max_mem: 4188M [05/11 11:06:00 d2.utils.events]: eta: 0:16:13 iter: 2899 total_loss: 15.19 loss_ce: 1.103 loss_bbox: 0.2365 loss_giou: 0.9256 loss_ce_0: 1.504 loss_bbox_0: 0.4395 loss_giou_0: 1.279 loss_ce_1: 1.222 loss_bbox_1: 0.3448 loss_giou_1: 1.117 loss_ce_2: 1.162 loss_bbox_2: 0.2908 loss_giou_2: 0.9648 loss_ce_3: 1.125 loss_bbox_3: 0.221 loss_giou_3: 0.8601 loss_ce_4: 1.114 loss_bbox_4: 0.2222 loss_giou_4: 0.8862 time: 0.4684 last_time: 0.3591 data_time: 0.0175 last_data_time: 0.0036 lr: 2.5e-07 max_mem: 4189M [05/11 11:06:09 d2.utils.events]: eta: 0:16:03 iter: 2919 total_loss: 16.14 loss_ce: 1.177 loss_bbox: 0.2488 loss_giou: 0.9901 loss_ce_0: 1.581 loss_bbox_0: 0.4737 loss_giou_0: 1.483 loss_ce_1: 1.259 loss_bbox_1: 0.3435 loss_giou_1: 1.139 loss_ce_2: 1.111 loss_bbox_2: 0.2833 loss_giou_2: 1.123 loss_ce_3: 1.209 loss_bbox_3: 0.2793 loss_giou_3: 1.077 loss_ce_4: 1.14 loss_bbox_4: 0.2468 loss_giou_4: 0.9795 time: 0.4683 last_time: 0.4238 data_time: 0.0216 last_data_time: 0.0247 lr: 2.5e-07 max_mem: 4189M [05/11 11:06:18 d2.utils.events]: eta: 0:15:52 iter: 2939 total_loss: 14.07 loss_ce: 1.022 loss_bbox: 0.2032 loss_giou: 0.8397 loss_ce_0: 1.466 loss_bbox_0: 0.4374 loss_giou_0: 1.336 loss_ce_1: 1.121 loss_bbox_1: 0.3034 loss_giou_1: 1.064 loss_ce_2: 1.03 loss_bbox_2: 0.2275 loss_giou_2: 0.8407 loss_ce_3: 1.004 loss_bbox_3: 0.2181 loss_giou_3: 0.8761 loss_ce_4: 1.032 loss_bbox_4: 0.2072 loss_giou_4: 0.8682 time: 0.4682 last_time: 0.6090 data_time: 0.0136 last_data_time: 0.0056 lr: 2.5e-07 max_mem: 4189M [05/11 11:06:27 d2.utils.events]: eta: 0:15:43 iter: 2959 total_loss: 14.88 loss_ce: 1.016 loss_bbox: 0.2088 loss_giou: 0.8485 loss_ce_0: 1.406 loss_bbox_0: 0.5361 loss_giou_0: 1.509 loss_ce_1: 1.142 loss_bbox_1: 0.359 loss_giou_1: 1.156 loss_ce_2: 1.006 loss_bbox_2: 0.3124 loss_giou_2: 0.988 loss_ce_3: 1.055 loss_bbox_3: 0.2405 loss_giou_3: 0.9191 loss_ce_4: 1.029 loss_bbox_4: 0.2193 loss_giou_4: 0.8009 time: 0.4682 last_time: 0.6083 data_time: 0.0237 last_data_time: 0.0375 lr: 2.5e-07 max_mem: 4189M [05/11 11:06:36 d2.utils.events]: eta: 0:15:29 iter: 2979 total_loss: 15.37 loss_ce: 1.096 loss_bbox: 0.2677 loss_giou: 0.8776 loss_ce_0: 1.494 loss_bbox_0: 0.4445 loss_giou_0: 1.361 loss_ce_1: 1.137 loss_bbox_1: 0.2767 loss_giou_1: 1.035 loss_ce_2: 1.104 loss_bbox_2: 0.2791 loss_giou_2: 1.006 loss_ce_3: 1.126 loss_bbox_3: 0.2635 loss_giou_3: 0.9505 loss_ce_4: 1.106 loss_bbox_4: 0.2859 loss_giou_4: 1.016 time: 0.4679 last_time: 0.4104 data_time: 0.0196 last_data_time: 0.0030 lr: 2.5e-07 max_mem: 4189M '''
And here is my config.yaml ''' CUDNN_BENCHMARK: false DATALOADER: ASPECT_RATIO_GROUPING: true FILTER_EMPTY_ANNOTATIONS: false NUM_WORKERS: 4 REPEAT_THRESHOLD: 0.0 SAMPLER_TRAIN: TrainingSampler DATASETS: PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 PROPOSAL_FILES_TEST: [] PROPOSAL_FILES_TRAIN: [] TEST:
And my result is very terrible '''
The text was updated successfully, but these errors were encountered:
No branches or pull requests
I successfully starting the training,but i meet some the problem which makes my accuracy very low.
'''
Above is part of my training logs,obviously my training loss keeps very high.
[05/11 11:05:31 d2.utils.events]: eta: 0:16:35 iter: 2839 total_loss: 13.81 loss_ce: 1.062 loss_bbox: 0.2362 loss_giou: 0.7347 loss_ce_0: 1.533 loss_bbox_0: 0.4347 loss_giou_0: 1.347 loss_ce_1: 1.076 loss_bbox_1: 0.3313 loss_giou_1: 0.9831 loss_ce_2: 0.983 loss_bbox_2: 0.2493 loss_giou_2: 0.8497 loss_ce_3: 1.095 loss_bbox_3: 0.2378 loss_giou_3: 0.8031 loss_ce_4: 1.037 loss_bbox_4: 0.2179 loss_giou_4: 0.7701 time: 0.4684 last_time: 0.4062 data_time: 0.0213 last_data_time: 0.0158 lr: 2.5e-07 max_mem: 4188M
[05/11 11:05:41 d2.utils.events]: eta: 0:16:28 iter: 2859 total_loss: 15.24 loss_ce: 1.078 loss_bbox: 0.2586 loss_giou: 0.9109 loss_ce_0: 1.416 loss_bbox_0: 0.5326 loss_giou_0: 1.461 loss_ce_1: 1.054 loss_bbox_1: 0.3595 loss_giou_1: 1.137 loss_ce_2: 1.06 loss_bbox_2: 0.3113 loss_giou_2: 1.046 loss_ce_3: 1.061 loss_bbox_3: 0.2689 loss_giou_3: 0.9477 loss_ce_4: 1.049 loss_bbox_4: 0.249 loss_giou_4: 0.9952 time: 0.4684 last_time: 0.5241 data_time: 0.0168 last_data_time: 0.0031 lr: 2.5e-07 max_mem: 4188M
[05/11 11:05:50 d2.utils.events]: eta: 0:16:21 iter: 2879 total_loss: 13.91 loss_ce: 0.9869 loss_bbox: 0.2141 loss_giou: 0.8199 loss_ce_0: 1.447 loss_bbox_0: 0.4575 loss_giou_0: 1.436 loss_ce_1: 1.119 loss_bbox_1: 0.3276 loss_giou_1: 1.095 loss_ce_2: 0.9777 loss_bbox_2: 0.257 loss_giou_2: 0.9116 loss_ce_3: 0.9843 loss_bbox_3: 0.2249 loss_giou_3: 0.8466 loss_ce_4: 0.9701 loss_bbox_4: 0.214 loss_giou_4: 0.8948 time: 0.4685 last_time: 0.4227 data_time: 0.0191 last_data_time: 0.0087 lr: 2.5e-07 max_mem: 4188M
[05/11 11:06:00 d2.utils.events]: eta: 0:16:13 iter: 2899 total_loss: 15.19 loss_ce: 1.103 loss_bbox: 0.2365 loss_giou: 0.9256 loss_ce_0: 1.504 loss_bbox_0: 0.4395 loss_giou_0: 1.279 loss_ce_1: 1.222 loss_bbox_1: 0.3448 loss_giou_1: 1.117 loss_ce_2: 1.162 loss_bbox_2: 0.2908 loss_giou_2: 0.9648 loss_ce_3: 1.125 loss_bbox_3: 0.221 loss_giou_3: 0.8601 loss_ce_4: 1.114 loss_bbox_4: 0.2222 loss_giou_4: 0.8862 time: 0.4684 last_time: 0.3591 data_time: 0.0175 last_data_time: 0.0036 lr: 2.5e-07 max_mem: 4189M
[05/11 11:06:09 d2.utils.events]: eta: 0:16:03 iter: 2919 total_loss: 16.14 loss_ce: 1.177 loss_bbox: 0.2488 loss_giou: 0.9901 loss_ce_0: 1.581 loss_bbox_0: 0.4737 loss_giou_0: 1.483 loss_ce_1: 1.259 loss_bbox_1: 0.3435 loss_giou_1: 1.139 loss_ce_2: 1.111 loss_bbox_2: 0.2833 loss_giou_2: 1.123 loss_ce_3: 1.209 loss_bbox_3: 0.2793 loss_giou_3: 1.077 loss_ce_4: 1.14 loss_bbox_4: 0.2468 loss_giou_4: 0.9795 time: 0.4683 last_time: 0.4238 data_time: 0.0216 last_data_time: 0.0247 lr: 2.5e-07 max_mem: 4189M
[05/11 11:06:18 d2.utils.events]: eta: 0:15:52 iter: 2939 total_loss: 14.07 loss_ce: 1.022 loss_bbox: 0.2032 loss_giou: 0.8397 loss_ce_0: 1.466 loss_bbox_0: 0.4374 loss_giou_0: 1.336 loss_ce_1: 1.121 loss_bbox_1: 0.3034 loss_giou_1: 1.064 loss_ce_2: 1.03 loss_bbox_2: 0.2275 loss_giou_2: 0.8407 loss_ce_3: 1.004 loss_bbox_3: 0.2181 loss_giou_3: 0.8761 loss_ce_4: 1.032 loss_bbox_4: 0.2072 loss_giou_4: 0.8682 time: 0.4682 last_time: 0.6090 data_time: 0.0136 last_data_time: 0.0056 lr: 2.5e-07 max_mem: 4189M
[05/11 11:06:27 d2.utils.events]: eta: 0:15:43 iter: 2959 total_loss: 14.88 loss_ce: 1.016 loss_bbox: 0.2088 loss_giou: 0.8485 loss_ce_0: 1.406 loss_bbox_0: 0.5361 loss_giou_0: 1.509 loss_ce_1: 1.142 loss_bbox_1: 0.359 loss_giou_1: 1.156 loss_ce_2: 1.006 loss_bbox_2: 0.3124 loss_giou_2: 0.988 loss_ce_3: 1.055 loss_bbox_3: 0.2405 loss_giou_3: 0.9191 loss_ce_4: 1.029 loss_bbox_4: 0.2193 loss_giou_4: 0.8009 time: 0.4682 last_time: 0.6083 data_time: 0.0237 last_data_time: 0.0375 lr: 2.5e-07 max_mem: 4189M
[05/11 11:06:36 d2.utils.events]: eta: 0:15:29 iter: 2979 total_loss: 15.37 loss_ce: 1.096 loss_bbox: 0.2677 loss_giou: 0.8776 loss_ce_0: 1.494 loss_bbox_0: 0.4445 loss_giou_0: 1.361 loss_ce_1: 1.137 loss_bbox_1: 0.2767 loss_giou_1: 1.035 loss_ce_2: 1.104 loss_bbox_2: 0.2791 loss_giou_2: 1.006 loss_ce_3: 1.126 loss_bbox_3: 0.2635 loss_giou_3: 0.9505 loss_ce_4: 1.106 loss_bbox_4: 0.2859 loss_giou_4: 1.016 time: 0.4679 last_time: 0.4104 data_time: 0.0196 last_data_time: 0.0030 lr: 2.5e-07 max_mem: 4189M
'''
And here is my config.yaml
'''
CUDNN_BENCHMARK: false
DATALOADER:
ASPECT_RATIO_GROUPING: true
FILTER_EMPTY_ANNOTATIONS: false
NUM_WORKERS: 4
REPEAT_THRESHOLD: 0.0
SAMPLER_TRAIN: TrainingSampler
DATASETS:
PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
PROPOSAL_FILES_TEST: []
PROPOSAL_FILES_TRAIN: []
TEST:
TRAIN:
GLOBAL:
HACK: 1.0
INPUT:
CROP:
ENABLED: false
SIZE:
TYPE: absolute_range
FORMAT: RGB
MASK_FORMAT: polygon
MAX_SIZE_TEST: 1800
MAX_SIZE_TRAIN: 1800
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN:
MIN_SIZE_TRAIN_SAMPLING: choice
RANDOM_FLIP: horizontal
MODEL:
ANCHOR_GENERATOR:
ANGLES:
ASPECT_RATIOS:
NAME: DefaultAnchorGenerator
OFFSET: 0.0
SIZES:
BACKBONE:
FREEZE_AT: 2
NAME: build_resnet_fpn_backbone
DEVICE: cuda
DiffusionDet:
ACTIVATION: relu
ALPHA: 0.25
CLASS_WEIGHT: 2.0
DEEP_SUPERVISION: true
DIM_DYNAMIC: 64
DIM_FEEDFORWARD: 2048
DROPOUT: 0.0
GAMMA: 2.0
GIOU_WEIGHT: 2.0
HIDDEN_DIM: 256
L1_WEIGHT: 5.0
NHEADS: 8
NO_OBJECT_WEIGHT: 0.1
NUM_CLASSES: 7
NUM_CLS: 1
NUM_DYNAMIC: 2
NUM_HEADS: 6
NUM_PROPOSALS: 500
NUM_REG: 3
OTA_K: 5
PRIOR_PROB: 0.01
SAMPLE_STEP: 1
SNR_SCALE: 2.0
USE_FED_LOSS: false
USE_FOCAL: true
USE_NMS: true
FPN:
FUSE_TYPE: sum
IN_FEATURES:
NORM: ''
OUT_CHANNELS: 256
KEYPOINT_ON: false
LOAD_PROPOSALS: false
MASK_ON: false
META_ARCHITECTURE: DiffusionDet
PANOPTIC_FPN:
COMBINE:
ENABLED: true
INSTANCES_CONFIDENCE_THRESH: 0.5
OVERLAP_THRESH: 0.5
STUFF_AREA_LIMIT: 4096
INSTANCE_LOSS_WEIGHT: 1.0
PIXEL_MEAN:
PIXEL_STD:
PROPOSAL_GENERATOR:
MIN_SIZE: 0
NAME: RPN
RESNETS:
DEFORM_MODULATED: false
DEFORM_NUM_GROUPS: 1
DEFORM_ON_PER_STAGE:
DEPTH: 50
NORM: FrozenBN
NUM_GROUPS: 1
OUT_FEATURES:
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: false
WIDTH_PER_GROUP: 64
RETINANET:
BBOX_REG_LOSS_TYPE: smooth_l1
BBOX_REG_WEIGHTS: &id002
FOCAL_LOSS_ALPHA: 0.25
FOCAL_LOSS_GAMMA: 2.0
IN_FEATURES:
IOU_LABELS:
IOU_THRESHOLDS:
NMS_THRESH_TEST: 0.5
NORM: ''
NUM_CLASSES: 7
NUM_CONVS: 4
PRIOR_PROB: 0.01
SCORE_THRESH_TEST: 0.05
SMOOTH_L1_LOSS_BETA: 0.1
TOPK_CANDIDATES_TEST: 1000
ROI_BOX_CASCADE_HEAD:
BBOX_REG_WEIGHTS:
IOUS:
ROI_BOX_HEAD:
BBOX_REG_LOSS_TYPE: smooth_l1
BBOX_REG_LOSS_WEIGHT: 1.0
BBOX_REG_WEIGHTS: *id001
CLS_AGNOSTIC_BBOX_REG: false
CONV_DIM: 256
FC_DIM: 1024
FED_LOSS_FREQ_WEIGHT_POWER: 0.5
FED_LOSS_NUM_CLASSES: 50
NAME: ''
NORM: ''
NUM_CONV: 0
NUM_FC: 0
POOLER_RESOLUTION: 7
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: ROIAlignV2
SMOOTH_L1_BETA: 0.0
TRAIN_ON_PRED_BOXES: false
USE_FED_LOSS: false
USE_SIGMOID_CE: false
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
IN_FEATURES:
IOU_LABELS:
IOU_THRESHOLDS:
NAME: Res5ROIHeads
NMS_THRESH_TEST: 0.5
NUM_CLASSES: 7
POSITIVE_FRACTION: 0.25
PROPOSAL_APPEND_GT: true
SCORE_THRESH_TEST: 0.05
ROI_KEYPOINT_HEAD:
CONV_DIMS:
LOSS_WEIGHT: 1.0
MIN_KEYPOINTS_PER_IMAGE: 1
NAME: KRCNNConvDeconvUpsampleHead
NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
NUM_KEYPOINTS: 17
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_TYPE: ROIAlignV2
ROI_MASK_HEAD:
CLS_AGNOSTIC_MASK: false
CONV_DIM: 256
NAME: MaskRCNNConvUpsampleHead
NORM: ''
NUM_CONV: 0
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_TYPE: ROIAlignV2
RPN:
BATCH_SIZE_PER_IMAGE: 256
BBOX_REG_LOSS_TYPE: smooth_l1
BBOX_REG_LOSS_WEIGHT: 1.0
BBOX_REG_WEIGHTS: *id002
BOUNDARY_THRESH: -1
CONV_DIMS:
HEAD_NAME: StandardRPNHead
IN_FEATURES:
IOU_LABELS:
IOU_THRESHOLDS:
LOSS_WEIGHT: 1.0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOPK_TEST: 1000
POST_NMS_TOPK_TRAIN: 2000
PRE_NMS_TOPK_TEST: 6000
PRE_NMS_TOPK_TRAIN: 12000
SMOOTH_L1_BETA: 0.0
SEM_SEG_HEAD:
COMMON_STRIDE: 4
CONVS_DIM: 128
IGNORE_VALUE: 255
IN_FEATURES:
LOSS_WEIGHT: 1.0
NAME: SemSegFPNHead
NORM: GN
NUM_CLASSES: 54
SWIN:
OUT_FEATURES:
SIZE: B
USE_CHECKPOINT: false
WEIGHTS: detectron2://ImageNetPretrained/torchvision/R-50.pkl
MODEL_EMA:
DECAY: 0.999
DEVICE: ''
ENABLED: false
USE_EMA_WEIGHTS_FOR_EVAL_ONLY: false
YOLOX: false
OUTPUT_DIR: ./output
SEED: 40244023
SOLVER:
AMP:
ENABLED: false
BACKBONE_MULTIPLIER: 1.0
BASE_LR: 5.0e-06
BASE_LR_END: 0.0
BIAS_LR_FACTOR: 1.0
CHECKPOINT_PERIOD: 5000
CLIP_GRADIENTS:
CLIP_TYPE: full_model
CLIP_VALUE: 1.0
ENABLED: true
NORM_TYPE: 2.0
GAMMA: 0.05
IMS_PER_BATCH: 1
LR_SCHEDULER_NAME: WarmupMultiStepLR
MAX_ITER: 5000
MOMENTUM: 0.9
NESTEROV: false
NUM_DECAYS: 3
OPTIMIZER: ADAMW
REFERENCE_WORLD_SIZE: 0
RESCALE_INTERVAL: false
STEPS:
WARMUP_FACTOR: 0.01
WARMUP_ITERS: 1000
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: null
WEIGHT_DECAY_NORM: 0.0
TEST:
AUG:
CVPODS_TTA: true
ENABLED: false
FLIP: true
MAX_SIZE: 4000
MIN_SIZES:
SCALE_FILTER: true
SCALE_RANGES:
DETECTIONS_PER_IMAGE: 100
EVAL_PERIOD: 3000
EXPECTED_RESULTS: []
KEYPOINT_OKS_SIGMAS: []
PRECISE_BN:
ENABLED: false
NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
'''
And my result is very terrible
'''
The text was updated successfully, but these errors were encountered: