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	[04/19 13:19:35] detectron2 INFO: Rank of current process: 0. World size: 1
	[04/19 13:19:36] detectron2 INFO: Environment info:
	---------------------- ----------------------------------------------------------------
	sys.platform linux
	Python 3.9.16 (main, Dec 7 2022, 01:11:51) [GCC 9.4.0]
	numpy 1.22.4
	detectron2 0.4 @/usr/local/lib/python3.9/dist-packages/detectron2
	Compiler GCC 9.4
	CUDA compiler CUDA 11.8
	detectron2 arch flags 7.5
	DETECTRON2_ENV_MODULE <not set>
	PyTorch 2.0.0+cu118 @/usr/local/lib/python3.9/dist-packages/torch
	PyTorch debug build False
	GPU available True
	GPU 0 Tesla T4 (arch=7.5)
	CUDA_HOME /usr/local/cuda
	Pillow 9.5.0
	torchvision 0.15.1+cu118 @/usr/local/lib/python3.9/dist-packages/torchvision
	torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
	fvcore 0.1.3.post20210317
	cv2 4.7.0
	---------------------- ----------------------------------------------------------------
	PyTorch built with:
	- GCC 9.3
	- C++ Version: 201703
	- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
	- Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
	- OpenMP 201511 (a.k.a. OpenMP 4.5)
	- LAPACK is enabled (usually provided by MKL)
	- NNPACK is enabled
	- CPU capability usage: AVX2
	- CUDA Runtime 11.8
	- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
	- CuDNN 8.7
	- Magma 2.6.1
	- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

	[04/19 13:19:36] detectron2 INFO: Command line arguments: Namespace(config_file='/content/layout-model-training/config_LayoutParser_PrimaDataset.yaml', resume=False, eval_only=False, num_gpus=1, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:49152', opts=['OUTPUT_DIR', '/content/drive/MyDrive/layoutparser/modele', 'SOLVER.IMS_PER_BATCH', '2'], dataset_name='modele', json_annotation_train='/content/drive/MyDrive/layoutparser/dataset6/train/via_project_19Apr2023_15h0m_coco.json', image_path_train='/content/drive/MyDrive/layoutparser/dataset6/train', json_annotation_val='/content/drive/MyDrive/layoutparser/dataset6/val/via_project_19Apr2023_15h9m_coco.json', image_path_val='/content/drive/MyDrive/layoutparser/dataset6/val')
	[04/19 13:19:36] detectron2 INFO: Contents of args.config_file=/content/layout-model-training/config_LayoutParser_PrimaDataset.yaml:
	CUDNN_BENCHMARK: false
	DATALOADER:
	ASPECT_RATIO_GROUPING: true
	FILTER_EMPTY_ANNOTATIONS: true
	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:
	- prima-layout-val
	TRAIN:
	- prima-layout-train
	GLOBAL:
	HACK: 1.0
	INPUT:
	CROP:
	ENABLED: false
	SIZE:
	- 0.9
	- 0.9
	TYPE: relative_range
	FORMAT: BGR
	MASK_FORMAT: polygon
	MAX_SIZE_TEST: 1333
	MAX_SIZE_TRAIN: 1333
	MIN_SIZE_TEST: 800
	MIN_SIZE_TRAIN:
	- 640
	- 672
	- 704
	- 736
	- 768
	- 800
	MIN_SIZE_TRAIN_SAMPLING: choice
	MODEL:
	ANCHOR_GENERATOR:
	ANGLES:
	- - -90
	- 0
	- 90
	ASPECT_RATIOS:
	- - 0.5
	- 1.0
	- 2.0
	NAME: DefaultAnchorGenerator
	OFFSET: 0.0
	SIZES:
	- - 32
	- - 64
	- - 128
	- - 256
	- - 512
	BACKBONE:
	FREEZE_AT: 2
	NAME: build_resnet_fpn_backbone
	DEVICE: cuda
	FPN:
	FUSE_TYPE: sum
	IN_FEATURES:
	- res2
	- res3
	- res4
	- res5
	NORM: ''
	OUT_CHANNELS: 256
	KEYPOINT_ON: false
	LOAD_PROPOSALS: false
	MASK_ON: true
	META_ARCHITECTURE: GeneralizedRCNN
	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:
	- 103.53
	- 116.28
	- 123.675
	PIXEL_STD:
	- 1.0
	- 1.0
	- 1.0
	PROPOSAL_GENERATOR:
	MIN_SIZE: 0
	NAME: RPN
	RESNETS:
	DEFORM_MODULATED: false
	DEFORM_NUM_GROUPS: 1
	DEFORM_ON_PER_STAGE:
	- false
	- false
	- false
	- false
	DEPTH: 50
	NORM: FrozenBN
	NUM_GROUPS: 1
	OUT_FEATURES:
	- res2
	- res3
	- res4
	- res5
	RES2_OUT_CHANNELS: 256
	RES5_DILATION: 1
	STEM_OUT_CHANNELS: 64
	STRIDE_IN_1X1: true
	WIDTH_PER_GROUP: 64
	RETINANET:
	BBOX_REG_WEIGHTS:
	- 1.0
	- 1.0
	- 1.0
	- 1.0
	FOCAL_LOSS_ALPHA: 0.25
	FOCAL_LOSS_GAMMA: 2.0
	IN_FEATURES:
	- p3
	- p4
	- p5
	- p6
	- p7
	IOU_LABELS:
	- 0
	- -1
	- 1
	IOU_THRESHOLDS:
	- 0.4
	- 0.5
	NMS_THRESH_TEST: 0.5
	NUM_CLASSES: 80
	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:
	- - 10.0
	- 10.0
	- 5.0
	- 5.0
	- - 20.0
	- 20.0
	- 10.0
	- 10.0
	- - 30.0
	- 30.0
	- 15.0
	- 15.0
	IOUS:
	- 0.5
	- 0.6
	- 0.7
	ROI_BOX_HEAD:
	BBOX_REG_WEIGHTS:
	- 10.0
	- 10.0
	- 5.0
	- 5.0
	CLS_AGNOSTIC_BBOX_REG: false
	CONV_DIM: 256
	FC_DIM: 1024
	NAME: FastRCNNConvFCHead
	NORM: ''
	NUM_CONV: 0
	NUM_FC: 2
	POOLER_RESOLUTION: 7
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	SMOOTH_L1_BETA: 0.0
	TRAIN_ON_PRED_BOXES: false
	ROI_HEADS:
	BATCH_SIZE_PER_IMAGE: 512
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	IOU_LABELS:
	- 0
	- 1
	IOU_THRESHOLDS:
	- 0.5
	NAME: StandardROIHeads
	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:
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	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: 4
	POOLER_RESOLUTION: 14
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	RPN:
	BATCH_SIZE_PER_IMAGE: 256
	BBOX_REG_WEIGHTS:
	- 1.0
	- 1.0
	- 1.0
	- 1.0
	BOUNDARY_THRESH: -1
	HEAD_NAME: StandardRPNHead
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	- p6
	IOU_LABELS:
	- 0
	- -1
	- 1
	IOU_THRESHOLDS:
	- 0.3
	- 0.7
	LOSS_WEIGHT: 1.0
	NMS_THRESH: 0.7
	POSITIVE_FRACTION: 0.5
	POST_NMS_TOPK_TEST: 1000
	POST_NMS_TOPK_TRAIN: 1000
	PRE_NMS_TOPK_TEST: 1000
	PRE_NMS_TOPK_TRAIN: 2000
	SMOOTH_L1_BETA: 0.0
	SEM_SEG_HEAD:
	COMMON_STRIDE: 4
	CONVS_DIM: 128
	IGNORE_VALUE: 255
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	LOSS_WEIGHT: 1.0
	NAME: SemSegFPNHead
	NORM: GN
	NUM_CLASSES: 54
	WEIGHTS: /content/drive/MyDrive/layoutparser/modele/modele3_NP?/
	OUTPUT_DIR: ../outputs/prima/mask_rcnn_R_50_FPN_3x/
	SEED: -1
	SOLVER:
	BASE_LR: 0.00025
	BIAS_LR_FACTOR: 1.0
	CHECKPOINT_PERIOD: 50
	CLIP_GRADIENTS:
	CLIP_TYPE: value
	CLIP_VALUE: 1.0
	ENABLED: false
	NORM_TYPE: 2.0
	GAMMA: 0.1
	IMS_PER_BATCH: 2
	LR_SCHEDULER_NAME: WarmupMultiStepLR
	MAX_ITER: 300
	MOMENTUM: 0.9
	NESTEROV: false
	STEPS:
	- 210000
	- 250000
	WARMUP_FACTOR: 0.001
	WARMUP_ITERS: 1000
	WARMUP_METHOD: linear
	WEIGHT_DECAY: 0.0001
	WEIGHT_DECAY_BIAS: 0.0001
	WEIGHT_DECAY_NORM: 0.0
	TEST:
	AUG:
	ENABLED: false
	FLIP: true
	MAX_SIZE: 4000
	MIN_SIZES:
	- 400
	- 500
	- 600
	- 700
	- 800
	- 900
	- 1000
	- 1100
	- 1200
	DETECTIONS_PER_IMAGE: 100
	EVAL_PERIOD: 0
	EXPECTED_RESULTS: []
	KEYPOINT_OKS_SIGMAS: []
	PRECISE_BN:
	ENABLED: false
	NUM_ITER: 200
	VERSION: 2
	VIS_PERIOD: 0

	[04/19 13:19:36] detectron2 INFO: Running with full config:
	CUDNN_BENCHMARK: False
	DATALOADER:
	ASPECT_RATIO_GROUPING: True
	FILTER_EMPTY_ANNOTATIONS: True
	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: ('modele-val',)
	TRAIN: ('modele-train',)
	GLOBAL:
	HACK: 1.0
	INPUT:
	CROP:
	ENABLED: False
	SIZE: [0.9, 0.9]
	TYPE: relative_range
	FORMAT: BGR
	MASK_FORMAT: polygon
	MAX_SIZE_TEST: 1333
	MAX_SIZE_TRAIN: 1333
	MIN_SIZE_TEST: 800
	MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
	MIN_SIZE_TRAIN_SAMPLING: choice
	RANDOM_FLIP: horizontal
	MODEL:
	ANCHOR_GENERATOR:
	ANGLES: [[-90, 0, 90]]
	ASPECT_RATIOS: [[0.5, 1.0, 2.0]]
	NAME: DefaultAnchorGenerator
	OFFSET: 0.0
	SIZES: [[32], [64], [128], [256], [512]]
	BACKBONE:
	FREEZE_AT: 2
	NAME: build_resnet_fpn_backbone
	DEVICE: cuda
	FPN:
	FUSE_TYPE: sum
	IN_FEATURES: ['res2', 'res3', 'res4', 'res5']
	NORM:
	OUT_CHANNELS: 256
	KEYPOINT_ON: False
	LOAD_PROPOSALS: False
	MASK_ON: True
	META_ARCHITECTURE: GeneralizedRCNN
	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: [103.53, 116.28, 123.675]
	PIXEL_STD: [1.0, 1.0, 1.0]
	PROPOSAL_GENERATOR:
	MIN_SIZE: 0
	NAME: RPN
	RESNETS:
	DEFORM_MODULATED: False
	DEFORM_NUM_GROUPS: 1
	DEFORM_ON_PER_STAGE: [False, False, False, False]
	DEPTH: 50
	NORM: FrozenBN
	NUM_GROUPS: 1
	OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']
	RES2_OUT_CHANNELS: 256
	RES5_DILATION: 1
	STEM_OUT_CHANNELS: 64
	STRIDE_IN_1X1: True
	WIDTH_PER_GROUP: 64
	RETINANET:
	BBOX_REG_LOSS_TYPE: smooth_l1
	BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
	FOCAL_LOSS_ALPHA: 0.25
	FOCAL_LOSS_GAMMA: 2.0
	IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']
	IOU_LABELS: [0, -1, 1]
	IOU_THRESHOLDS: [0.4, 0.5]
	NMS_THRESH_TEST: 0.5
	NORM:
	NUM_CLASSES: 80
	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: ([10.0, 10.0, 5.0, 5.0], [20.0, 20.0, 10.0, 10.0], [30.0, 30.0, 15.0, 15.0])
	IOUS: (0.5, 0.6, 0.7)
	ROI_BOX_HEAD:
	BBOX_REG_LOSS_TYPE: smooth_l1
	BBOX_REG_LOSS_WEIGHT: 1.0
	BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
	CLS_AGNOSTIC_BBOX_REG: False
	CONV_DIM: 256
	FC_DIM: 1024
	NAME: FastRCNNConvFCHead
	NORM:
	NUM_CONV: 0
	NUM_FC: 2
	POOLER_RESOLUTION: 7
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	SMOOTH_L1_BETA: 0.0
	TRAIN_ON_PRED_BOXES: False
	ROI_HEADS:
	BATCH_SIZE_PER_IMAGE: 512
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
	IOU_LABELS: [0, 1]
	IOU_THRESHOLDS: [0.5]
	NAME: StandardROIHeads
	NMS_THRESH_TEST: 0.5
	NUM_CLASSES: 2
	POSITIVE_FRACTION: 0.25
	PROPOSAL_APPEND_GT: True
	SCORE_THRESH_TEST: 0.05
	ROI_KEYPOINT_HEAD:
	CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)
	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: 4
	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: (1.0, 1.0, 1.0, 1.0)
	BOUNDARY_THRESH: -1
	HEAD_NAME: StandardRPNHead
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']
	IOU_LABELS: [0, -1, 1]
	IOU_THRESHOLDS: [0.3, 0.7]
	LOSS_WEIGHT: 1.0
	NMS_THRESH: 0.7
	POSITIVE_FRACTION: 0.5
	POST_NMS_TOPK_TEST: 1000
	POST_NMS_TOPK_TRAIN: 1000
	PRE_NMS_TOPK_TEST: 1000
	PRE_NMS_TOPK_TRAIN: 2000
	SMOOTH_L1_BETA: 0.0
	SEM_SEG_HEAD:
	COMMON_STRIDE: 4
	CONVS_DIM: 128
	IGNORE_VALUE: 255
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
	LOSS_WEIGHT: 1.0
	NAME: SemSegFPNHead
	NORM: GN
	NUM_CLASSES: 54
	WEIGHTS: /content/drive/MyDrive/layoutparser/modele/modele3_NP?/
	OUTPUT_DIR: /content/drive/MyDrive/layoutparser/modele
	SEED: -1
	SOLVER:
	AMP:
	ENABLED: False
	BASE_LR: 0.00025
	BIAS_LR_FACTOR: 1.0
	CHECKPOINT_PERIOD: 50
	CLIP_GRADIENTS:
	CLIP_TYPE: value
	CLIP_VALUE: 1.0
	ENABLED: False
	NORM_TYPE: 2.0
	GAMMA: 0.1
	IMS_PER_BATCH: 2
	LR_SCHEDULER_NAME: WarmupMultiStepLR
	MAX_ITER: 300
	MOMENTUM: 0.9
	NESTEROV: False
	REFERENCE_WORLD_SIZE: 0
	STEPS: (210000, 250000)
	WARMUP_FACTOR: 0.001
	WARMUP_ITERS: 1000
	WARMUP_METHOD: linear
	WEIGHT_DECAY: 0.0001
	WEIGHT_DECAY_BIAS: 0.0001
	WEIGHT_DECAY_NORM: 0.0
	TEST:
	AUG:
	ENABLED: False
	FLIP: True
	MAX_SIZE: 4000
	MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
	DETECTIONS_PER_IMAGE: 100
	EVAL_PERIOD: 0
	EXPECTED_RESULTS: []
	KEYPOINT_OKS_SIGMAS: []
	PRECISE_BN:
	ENABLED: False
	NUM_ITER: 200
	VERSION: 2
	VIS_PERIOD: 0
	[04/19 13:19:36] detectron2 INFO: Full config saved to /content/drive/MyDrive/layoutparser/modele/config.yaml
	[04/19 13:19:36] d2.utils.env INFO: Using a generated random seed 36661240
	[04/19 13:19:43] d2.engine.defaults INFO: Model:
	GeneralizedRCNN(
	(backbone): FPN(
	(fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(top_block): LastLevelMaxPool()
	(bottom_up): ResNet(
	(stem): BasicStem(
	(conv1): Conv2d(
	3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	)
	(res2): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv1): Conv2d(
	64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	)
	(res3): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv1): Conv2d(
	256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(3): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	)
	(res4): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	(conv1): Conv2d(
	512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(3): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(4): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(5): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	)
	(res5): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	(conv1): Conv2d(
	1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	)
	)
	)
	(proposal_generator): RPN(
	(rpn_head): StandardRPNHead(
	(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
	(anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
	)
	(anchor_generator): DefaultAnchorGenerator(
	(cell_anchors): BufferList()
	)
	)
	(roi_heads): StandardROIHeads(
	(box_pooler): ROIPooler(
	(level_poolers): ModuleList(
	(0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
	(1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
	(2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
	(3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
	)
	)
	(box_head): FastRCNNConvFCHead(
	(flatten): Flatten(start_dim=1, end_dim=-1)
	(fc1): Linear(in_features=12544, out_features=1024, bias=True)
	(fc_relu1): ReLU()
	(fc2): Linear(in_features=1024, out_features=1024, bias=True)
	(fc_relu2): ReLU()
	)
	(box_predictor): FastRCNNOutputLayers(
	(cls_score): Linear(in_features=1024, out_features=3, bias=True)
	(bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
	)
	(mask_pooler): ROIPooler(
	(level_poolers): ModuleList(
	(0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)
	(1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)
	(2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
	(3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
	)
	)
	(mask_head): MaskRCNNConvUpsampleHead(
	(mask_fcn1): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn3): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn4): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
	(deconv_relu): ReLU()
	(predictor): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))
	)
	)
	)
	[04/19 13:19:43] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip(), RandomRotation(angle=[-90.0, 0.0])]
	[04/19 13:19:43] d2.data.datasets.coco INFO: Loaded 36 images in COCO format from /content/drive/MyDrive/layoutparser/dataset6/train/via_project_19Apr2023_15h0m_coco.json
	[04/19 13:19:43] d2.data.build INFO: Removed 6 images with no usable annotations. 30 images left.
	[04/19 13:19:43] d2.data.build INFO: Distribution of instances among all 2 categories:
	[36m\| category \| #instances \| category \| #instances \|
	\|:----------:\|:-------------\|:----------:\|:-------------\|
	\| \| 89 \| \| 0 \|
	\| \| \| \| \|
	\| total \| 89 \| \| \|[0m
	[04/19 13:19:43] d2.data.build INFO: Using training sampler TrainingSampler
	[04/19 13:19:43] d2.data.common INFO: Serializing 30 elements to byte tensors and concatenating them all ...
	[04/19 13:19:43] d2.data.common INFO: Serialized dataset takes 0.01 MiB
	[04/19 13:19:43] d2.solver.build WARNING: SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored.
	[04/19 13:19:45] fvcore.common.checkpoint INFO: Loading checkpoint from /content/drive/MyDrive/layoutparser/modele/modele3_NP?/
	[04/19 13:20:18] detectron2 INFO: Rank of current process: 0. World size: 1
	[04/19 13:20:20] detectron2 INFO: Environment info:
	---------------------- ----------------------------------------------------------------
	sys.platform linux
	Python 3.9.16 (main, Dec 7 2022, 01:11:51) [GCC 9.4.0]
	numpy 1.22.4
	detectron2 0.4 @/usr/local/lib/python3.9/dist-packages/detectron2
	Compiler GCC 9.4
	CUDA compiler CUDA 11.8
	detectron2 arch flags 7.5
	DETECTRON2_ENV_MODULE <not set>
	PyTorch 2.0.0+cu118 @/usr/local/lib/python3.9/dist-packages/torch
	PyTorch debug build False
	GPU available True
	GPU 0 Tesla T4 (arch=7.5)
	CUDA_HOME /usr/local/cuda
	Pillow 9.5.0
	torchvision 0.15.1+cu118 @/usr/local/lib/python3.9/dist-packages/torchvision
	torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
	fvcore 0.1.3.post20210317
	cv2 4.7.0
	---------------------- ----------------------------------------------------------------
	PyTorch built with:
	- GCC 9.3
	- C++ Version: 201703
	- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
	- Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
	- OpenMP 201511 (a.k.a. OpenMP 4.5)
	- LAPACK is enabled (usually provided by MKL)
	- NNPACK is enabled
	- CPU capability usage: AVX2
	- CUDA Runtime 11.8
	- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
	- CuDNN 8.7
	- Magma 2.6.1
	- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

	[04/19 13:20:20] detectron2 INFO: Command line arguments: Namespace(config_file='/content/layout-model-training/config_LayoutParser_PrimaDataset.yaml', resume=False, eval_only=False, num_gpus=1, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:49152', opts=['OUTPUT_DIR', '/content/drive/MyDrive/layoutparser/modele', 'SOLVER.IMS_PER_BATCH', '2'], dataset_name='modele', json_annotation_train='/content/drive/MyDrive/layoutparser/dataset6/train/via_project_19Apr2023_15h0m_coco.json', image_path_train='/content/drive/MyDrive/layoutparser/dataset6/train', json_annotation_val='/content/drive/MyDrive/layoutparser/dataset6/val/via_project_19Apr2023_15h9m_coco.json', image_path_val='/content/drive/MyDrive/layoutparser/dataset6/val')
	[04/19 13:20:20] detectron2 INFO: Contents of args.config_file=/content/layout-model-training/config_LayoutParser_PrimaDataset.yaml:
	CUDNN_BENCHMARK: false
	DATALOADER:
	ASPECT_RATIO_GROUPING: true
	FILTER_EMPTY_ANNOTATIONS: true
	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:
	- prima-layout-val
	TRAIN:
	- prima-layout-train
	GLOBAL:
	HACK: 1.0
	INPUT:
	CROP:
	ENABLED: false
	SIZE:
	- 0.9
	- 0.9
	TYPE: relative_range
	FORMAT: BGR
	MASK_FORMAT: polygon
	MAX_SIZE_TEST: 1333
	MAX_SIZE_TRAIN: 1333
	MIN_SIZE_TEST: 800
	MIN_SIZE_TRAIN:
	- 640
	- 672
	- 704
	- 736
	- 768
	- 800
	MIN_SIZE_TRAIN_SAMPLING: choice
	MODEL:
	ANCHOR_GENERATOR:
	ANGLES:
	- - -90
	- 0
	- 90
	ASPECT_RATIOS:
	- - 0.5
	- 1.0
	- 2.0
	NAME: DefaultAnchorGenerator
	OFFSET: 0.0
	SIZES:
	- - 32
	- - 64
	- - 128
	- - 256
	- - 512
	BACKBONE:
	FREEZE_AT: 2
	NAME: build_resnet_fpn_backbone
	DEVICE: cuda
	FPN:
	FUSE_TYPE: sum
	IN_FEATURES:
	- res2
	- res3
	- res4
	- res5
	NORM: ''
	OUT_CHANNELS: 256
	KEYPOINT_ON: false
	LOAD_PROPOSALS: false
	MASK_ON: true
	META_ARCHITECTURE: GeneralizedRCNN
	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:
	- 103.53
	- 116.28
	- 123.675
	PIXEL_STD:
	- 1.0
	- 1.0
	- 1.0
	PROPOSAL_GENERATOR:
	MIN_SIZE: 0
	NAME: RPN
	RESNETS:
	DEFORM_MODULATED: false
	DEFORM_NUM_GROUPS: 1
	DEFORM_ON_PER_STAGE:
	- false
	- false
	- false
	- false
	DEPTH: 50
	NORM: FrozenBN
	NUM_GROUPS: 1
	OUT_FEATURES:
	- res2
	- res3
	- res4
	- res5
	RES2_OUT_CHANNELS: 256
	RES5_DILATION: 1
	STEM_OUT_CHANNELS: 64
	STRIDE_IN_1X1: true
	WIDTH_PER_GROUP: 64
	RETINANET:
	BBOX_REG_WEIGHTS:
	- 1.0
	- 1.0
	- 1.0
	- 1.0
	FOCAL_LOSS_ALPHA: 0.25
	FOCAL_LOSS_GAMMA: 2.0
	IN_FEATURES:
	- p3
	- p4
	- p5
	- p6
	- p7
	IOU_LABELS:
	- 0
	- -1
	- 1
	IOU_THRESHOLDS:
	- 0.4
	- 0.5
	NMS_THRESH_TEST: 0.5
	NUM_CLASSES: 80
	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:
	- - 10.0
	- 10.0
	- 5.0
	- 5.0
	- - 20.0
	- 20.0
	- 10.0
	- 10.0
	- - 30.0
	- 30.0
	- 15.0
	- 15.0
	IOUS:
	- 0.5
	- 0.6
	- 0.7
	ROI_BOX_HEAD:
	BBOX_REG_WEIGHTS:
	- 10.0
	- 10.0
	- 5.0
	- 5.0
	CLS_AGNOSTIC_BBOX_REG: false
	CONV_DIM: 256
	FC_DIM: 1024
	NAME: FastRCNNConvFCHead
	NORM: ''
	NUM_CONV: 0
	NUM_FC: 2
	POOLER_RESOLUTION: 7
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	SMOOTH_L1_BETA: 0.0
	TRAIN_ON_PRED_BOXES: false
	ROI_HEADS:
	BATCH_SIZE_PER_IMAGE: 512
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	IOU_LABELS:
	- 0
	- 1
	IOU_THRESHOLDS:
	- 0.5
	NAME: StandardROIHeads
	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:
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	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: 4
	POOLER_RESOLUTION: 14
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	RPN:
	BATCH_SIZE_PER_IMAGE: 256
	BBOX_REG_WEIGHTS:
	- 1.0
	- 1.0
	- 1.0
	- 1.0
	BOUNDARY_THRESH: -1
	HEAD_NAME: StandardRPNHead
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	- p6
	IOU_LABELS:
	- 0
	- -1
	- 1
	IOU_THRESHOLDS:
	- 0.3
	- 0.7
	LOSS_WEIGHT: 1.0
	NMS_THRESH: 0.7
	POSITIVE_FRACTION: 0.5
	POST_NMS_TOPK_TEST: 1000
	POST_NMS_TOPK_TRAIN: 1000
	PRE_NMS_TOPK_TEST: 1000
	PRE_NMS_TOPK_TRAIN: 2000
	SMOOTH_L1_BETA: 0.0
	SEM_SEG_HEAD:
	COMMON_STRIDE: 4
	CONVS_DIM: 128
	IGNORE_VALUE: 255
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	LOSS_WEIGHT: 1.0
	NAME: SemSegFPNHead
	NORM: GN
	NUM_CLASSES: 54
	WEIGHTS: /content/drive/MyDrive/layoutparser/modele/modele3_NP?/modele_final.pth
	OUTPUT_DIR: ../outputs/prima/mask_rcnn_R_50_FPN_3x/
	SEED: -1
	SOLVER:
	BASE_LR: 0.00025
	BIAS_LR_FACTOR: 1.0
	CHECKPOINT_PERIOD: 50
	CLIP_GRADIENTS:
	CLIP_TYPE: value
	CLIP_VALUE: 1.0
	ENABLED: false
	NORM_TYPE: 2.0
	GAMMA: 0.1
	IMS_PER_BATCH: 2
	LR_SCHEDULER_NAME: WarmupMultiStepLR
	MAX_ITER: 300
	MOMENTUM: 0.9
	NESTEROV: false
	STEPS:
	- 210000
	- 250000
	WARMUP_FACTOR: 0.001
	WARMUP_ITERS: 1000
	WARMUP_METHOD: linear
	WEIGHT_DECAY: 0.0001
	WEIGHT_DECAY_BIAS: 0.0001
	WEIGHT_DECAY_NORM: 0.0
	TEST:
	AUG:
	ENABLED: false
	FLIP: true
	MAX_SIZE: 4000
	MIN_SIZES:
	- 400
	- 500
	- 600
	- 700
	- 800
	- 900
	- 1000
	- 1100
	- 1200
	DETECTIONS_PER_IMAGE: 100
	EVAL_PERIOD: 0
	EXPECTED_RESULTS: []
	KEYPOINT_OKS_SIGMAS: []
	PRECISE_BN:
	ENABLED: false
	NUM_ITER: 200
	VERSION: 2
	VIS_PERIOD: 0

	[04/19 13:20:20] detectron2 INFO: Running with full config:
	CUDNN_BENCHMARK: False
	DATALOADER:
	ASPECT_RATIO_GROUPING: True
	FILTER_EMPTY_ANNOTATIONS: True
	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: ('modele-val',)
	TRAIN: ('modele-train',)
	GLOBAL:
	HACK: 1.0
	INPUT:
	CROP:
	ENABLED: False
	SIZE: [0.9, 0.9]
	TYPE: relative_range
	FORMAT: BGR
	MASK_FORMAT: polygon
	MAX_SIZE_TEST: 1333
	MAX_SIZE_TRAIN: 1333
	MIN_SIZE_TEST: 800
	MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
	MIN_SIZE_TRAIN_SAMPLING: choice
	RANDOM_FLIP: horizontal
	MODEL:
	ANCHOR_GENERATOR:
	ANGLES: [[-90, 0, 90]]
	ASPECT_RATIOS: [[0.5, 1.0, 2.0]]
	NAME: DefaultAnchorGenerator
	OFFSET: 0.0
	SIZES: [[32], [64], [128], [256], [512]]
	BACKBONE:
	FREEZE_AT: 2
	NAME: build_resnet_fpn_backbone
	DEVICE: cuda
	FPN:
	FUSE_TYPE: sum
	IN_FEATURES: ['res2', 'res3', 'res4', 'res5']
	NORM:
	OUT_CHANNELS: 256
	KEYPOINT_ON: False
	LOAD_PROPOSALS: False
	MASK_ON: True
	META_ARCHITECTURE: GeneralizedRCNN
	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: [103.53, 116.28, 123.675]
	PIXEL_STD: [1.0, 1.0, 1.0]
	PROPOSAL_GENERATOR:
	MIN_SIZE: 0
	NAME: RPN
	RESNETS:
	DEFORM_MODULATED: False
	DEFORM_NUM_GROUPS: 1
	DEFORM_ON_PER_STAGE: [False, False, False, False]
	DEPTH: 50
	NORM: FrozenBN
	NUM_GROUPS: 1
	OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']
	RES2_OUT_CHANNELS: 256
	RES5_DILATION: 1
	STEM_OUT_CHANNELS: 64
	STRIDE_IN_1X1: True
	WIDTH_PER_GROUP: 64
	RETINANET:
	BBOX_REG_LOSS_TYPE: smooth_l1
	BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
	FOCAL_LOSS_ALPHA: 0.25
	FOCAL_LOSS_GAMMA: 2.0
	IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']
	IOU_LABELS: [0, -1, 1]
	IOU_THRESHOLDS: [0.4, 0.5]
	NMS_THRESH_TEST: 0.5
	NORM:
	NUM_CLASSES: 80
	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: ([10.0, 10.0, 5.0, 5.0], [20.0, 20.0, 10.0, 10.0], [30.0, 30.0, 15.0, 15.0])
	IOUS: (0.5, 0.6, 0.7)
	ROI_BOX_HEAD:
	BBOX_REG_LOSS_TYPE: smooth_l1
	BBOX_REG_LOSS_WEIGHT: 1.0
	BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
	CLS_AGNOSTIC_BBOX_REG: False
	CONV_DIM: 256
	FC_DIM: 1024
	NAME: FastRCNNConvFCHead
	NORM:
	NUM_CONV: 0
	NUM_FC: 2
	POOLER_RESOLUTION: 7
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	SMOOTH_L1_BETA: 0.0
	TRAIN_ON_PRED_BOXES: False
	ROI_HEADS:
	BATCH_SIZE_PER_IMAGE: 512
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
	IOU_LABELS: [0, 1]
	IOU_THRESHOLDS: [0.5]
	NAME: StandardROIHeads
	NMS_THRESH_TEST: 0.5
	NUM_CLASSES: 2
	POSITIVE_FRACTION: 0.25
	PROPOSAL_APPEND_GT: True
	SCORE_THRESH_TEST: 0.05
	ROI_KEYPOINT_HEAD:
	CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)
	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: 4
	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: (1.0, 1.0, 1.0, 1.0)
	BOUNDARY_THRESH: -1
	HEAD_NAME: StandardRPNHead
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']
	IOU_LABELS: [0, -1, 1]
	IOU_THRESHOLDS: [0.3, 0.7]
	LOSS_WEIGHT: 1.0
	NMS_THRESH: 0.7
	POSITIVE_FRACTION: 0.5
	POST_NMS_TOPK_TEST: 1000
	POST_NMS_TOPK_TRAIN: 1000
	PRE_NMS_TOPK_TEST: 1000
	PRE_NMS_TOPK_TRAIN: 2000
	SMOOTH_L1_BETA: 0.0
	SEM_SEG_HEAD:
	COMMON_STRIDE: 4
	CONVS_DIM: 128
	IGNORE_VALUE: 255
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
	LOSS_WEIGHT: 1.0
	NAME: SemSegFPNHead
	NORM: GN
	NUM_CLASSES: 54
	WEIGHTS: /content/drive/MyDrive/layoutparser/modele/modele3_NP?/modele_final.pth
	OUTPUT_DIR: /content/drive/MyDrive/layoutparser/modele
	SEED: -1
	SOLVER:
	AMP:
	ENABLED: False
	BASE_LR: 0.00025
	BIAS_LR_FACTOR: 1.0
	CHECKPOINT_PERIOD: 50
	CLIP_GRADIENTS:
	CLIP_TYPE: value
	CLIP_VALUE: 1.0
	ENABLED: False
	NORM_TYPE: 2.0
	GAMMA: 0.1
	IMS_PER_BATCH: 2
	LR_SCHEDULER_NAME: WarmupMultiStepLR
	MAX_ITER: 300
	MOMENTUM: 0.9
	NESTEROV: False
	REFERENCE_WORLD_SIZE: 0
	STEPS: (210000, 250000)
	WARMUP_FACTOR: 0.001
	WARMUP_ITERS: 1000
	WARMUP_METHOD: linear
	WEIGHT_DECAY: 0.0001
	WEIGHT_DECAY_BIAS: 0.0001
	WEIGHT_DECAY_NORM: 0.0
	TEST:
	AUG:
	ENABLED: False
	FLIP: True
	MAX_SIZE: 4000
	MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
	DETECTIONS_PER_IMAGE: 100
	EVAL_PERIOD: 0
	EXPECTED_RESULTS: []
	KEYPOINT_OKS_SIGMAS: []
	PRECISE_BN:
	ENABLED: False
	NUM_ITER: 200
	VERSION: 2
	VIS_PERIOD: 0
	[04/19 13:20:20] detectron2 INFO: Full config saved to /content/drive/MyDrive/layoutparser/modele/config.yaml
	[04/19 13:20:20] d2.utils.env INFO: Using a generated random seed 20261058
	[04/19 13:20:22] d2.engine.defaults INFO: Model:
	GeneralizedRCNN(
	(backbone): FPN(
	(fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(top_block): LastLevelMaxPool()
	(bottom_up): ResNet(
	(stem): BasicStem(
	(conv1): Conv2d(
	3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	)
	(res2): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv1): Conv2d(
	64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	)
	(res3): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv1): Conv2d(
	256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(3): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	)
	(res4): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	(conv1): Conv2d(
	512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(3): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(4): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(5): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	)
	(res5): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	(conv1): Conv2d(
	1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	)
	)
	)
	(proposal_generator): RPN(
	(rpn_head): StandardRPNHead(
	(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
	(anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
	)
	(anchor_generator): DefaultAnchorGenerator(
	(cell_anchors): BufferList()
	)
	)
	(roi_heads): StandardROIHeads(
	(box_pooler): ROIPooler(
	(level_poolers): ModuleList(
	(0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
	(1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
	(2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
	(3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
	)
	)
	(box_head): FastRCNNConvFCHead(
	(flatten): Flatten(start_dim=1, end_dim=-1)
	(fc1): Linear(in_features=12544, out_features=1024, bias=True)
	(fc_relu1): ReLU()
	(fc2): Linear(in_features=1024, out_features=1024, bias=True)
	(fc_relu2): ReLU()
	)
	(box_predictor): FastRCNNOutputLayers(
	(cls_score): Linear(in_features=1024, out_features=3, bias=True)
	(bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
	)
	(mask_pooler): ROIPooler(
	(level_poolers): ModuleList(
	(0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)
	(1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)
	(2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
	(3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
	)
	)
	(mask_head): MaskRCNNConvUpsampleHead(
	(mask_fcn1): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn3): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn4): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
	(deconv_relu): ReLU()
	(predictor): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))
	)
	)
	)
	[04/19 13:20:22] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip(), RandomRotation(angle=[-90.0, 0.0])]
	[04/19 13:20:22] d2.data.datasets.coco INFO: Loaded 36 images in COCO format from /content/drive/MyDrive/layoutparser/dataset6/train/via_project_19Apr2023_15h0m_coco.json
	[04/19 13:20:22] d2.data.build INFO: Removed 6 images with no usable annotations. 30 images left.
	[04/19 13:20:22] d2.data.build INFO: Distribution of instances among all 2 categories:
	[36m\| category \| #instances \| category \| #instances \|
	\|:----------:\|:-------------\|:----------:\|:-------------\|
	\| \| 89 \| \| 0 \|
	\| \| \| \| \|
	\| total \| 89 \| \| \|[0m
	[04/19 13:20:22] d2.data.build INFO: Using training sampler TrainingSampler
	[04/19 13:20:22] d2.data.common INFO: Serializing 30 elements to byte tensors and concatenating them all ...
	[04/19 13:20:22] d2.data.common INFO: Serialized dataset takes 0.01 MiB
	[04/19 13:20:22] d2.solver.build WARNING: SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored.
	[04/19 13:20:24] fvcore.common.checkpoint INFO: Loading checkpoint from /content/drive/MyDrive/layoutparser/modele/modele3_NP?/modele_final.pth
	[04/19 13:21:19] detectron2 INFO: Rank of current process: 0. World size: 1
	[04/19 13:21:20] detectron2 INFO: Environment info:
	---------------------- ----------------------------------------------------------------
	sys.platform linux
	Python 3.9.16 (main, Dec 7 2022, 01:11:51) [GCC 9.4.0]
	numpy 1.22.4
	detectron2 0.4 @/usr/local/lib/python3.9/dist-packages/detectron2
	Compiler GCC 9.4
	CUDA compiler CUDA 11.8
	detectron2 arch flags 7.5
	DETECTRON2_ENV_MODULE <not set>
	PyTorch 2.0.0+cu118 @/usr/local/lib/python3.9/dist-packages/torch
	PyTorch debug build False
	GPU available True
	GPU 0 Tesla T4 (arch=7.5)
	CUDA_HOME /usr/local/cuda
	Pillow 9.5.0
	torchvision 0.15.1+cu118 @/usr/local/lib/python3.9/dist-packages/torchvision
	torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
	fvcore 0.1.3.post20210317
	cv2 4.7.0
	---------------------- ----------------------------------------------------------------
	PyTorch built with:
	- GCC 9.3
	- C++ Version: 201703
	- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
	- Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
	- OpenMP 201511 (a.k.a. OpenMP 4.5)
	- LAPACK is enabled (usually provided by MKL)
	- NNPACK is enabled
	- CPU capability usage: AVX2
	- CUDA Runtime 11.8
	- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
	- CuDNN 8.7
	- Magma 2.6.1
	- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

	[04/19 13:21:20] detectron2 INFO: Command line arguments: Namespace(config_file='/content/layout-model-training/config_LayoutParser_PrimaDataset.yaml', resume=False, eval_only=False, num_gpus=1, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:49152', opts=['OUTPUT_DIR', '/content/drive/MyDrive/layoutparser/modele', 'SOLVER.IMS_PER_BATCH', '2'], dataset_name='modele', json_annotation_train='/content/drive/MyDrive/layoutparser/dataset6/train/via_project_19Apr2023_15h0m_coco.json', image_path_train='/content/drive/MyDrive/layoutparser/dataset6/train', json_annotation_val='/content/drive/MyDrive/layoutparser/dataset6/val/via_project_19Apr2023_15h9m_coco.json', image_path_val='/content/drive/MyDrive/layoutparser/dataset6/val')
	[04/19 13:21:20] detectron2 INFO: Contents of args.config_file=/content/layout-model-training/config_LayoutParser_PrimaDataset.yaml:
	CUDNN_BENCHMARK: false
	DATALOADER:
	ASPECT_RATIO_GROUPING: true
	FILTER_EMPTY_ANNOTATIONS: true
	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:
	- prima-layout-val
	TRAIN:
	- prima-layout-train
	GLOBAL:
	HACK: 1.0
	INPUT:
	CROP:
	ENABLED: false
	SIZE:
	- 0.9
	- 0.9
	TYPE: relative_range
	FORMAT: BGR
	MASK_FORMAT: polygon
	MAX_SIZE_TEST: 1333
	MAX_SIZE_TRAIN: 1333
	MIN_SIZE_TEST: 800
	MIN_SIZE_TRAIN:
	- 640
	- 672
	- 704
	- 736
	- 768
	- 800
	MIN_SIZE_TRAIN_SAMPLING: choice
	MODEL:
	ANCHOR_GENERATOR:
	ANGLES:
	- - -90
	- 0
	- 90
	ASPECT_RATIOS:
	- - 0.5
	- 1.0
	- 2.0
	NAME: DefaultAnchorGenerator
	OFFSET: 0.0
	SIZES:
	- - 32
	- - 64
	- - 128
	- - 256
	- - 512
	BACKBONE:
	FREEZE_AT: 2
	NAME: build_resnet_fpn_backbone
	DEVICE: cuda
	FPN:
	FUSE_TYPE: sum
	IN_FEATURES:
	- res2
	- res3
	- res4
	- res5
	NORM: ''
	OUT_CHANNELS: 256
	KEYPOINT_ON: false
	LOAD_PROPOSALS: false
	MASK_ON: true
	META_ARCHITECTURE: GeneralizedRCNN
	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:
	- 103.53
	- 116.28
	- 123.675
	PIXEL_STD:
	- 1.0
	- 1.0
	- 1.0
	PROPOSAL_GENERATOR:
	MIN_SIZE: 0
	NAME: RPN
	RESNETS:
	DEFORM_MODULATED: false
	DEFORM_NUM_GROUPS: 1
	DEFORM_ON_PER_STAGE:
	- false
	- false
	- false
	- false
	DEPTH: 50
	NORM: FrozenBN
	NUM_GROUPS: 1
	OUT_FEATURES:
	- res2
	- res3
	- res4
	- res5
	RES2_OUT_CHANNELS: 256
	RES5_DILATION: 1
	STEM_OUT_CHANNELS: 64
	STRIDE_IN_1X1: true
	WIDTH_PER_GROUP: 64
	RETINANET:
	BBOX_REG_WEIGHTS:
	- 1.0
	- 1.0
	- 1.0
	- 1.0
	FOCAL_LOSS_ALPHA: 0.25
	FOCAL_LOSS_GAMMA: 2.0
	IN_FEATURES:
	- p3
	- p4
	- p5
	- p6
	- p7
	IOU_LABELS:
	- 0
	- -1
	- 1
	IOU_THRESHOLDS:
	- 0.4
	- 0.5
	NMS_THRESH_TEST: 0.5
	NUM_CLASSES: 80
	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:
	- - 10.0
	- 10.0
	- 5.0
	- 5.0
	- - 20.0
	- 20.0
	- 10.0
	- 10.0
	- - 30.0
	- 30.0
	- 15.0
	- 15.0
	IOUS:
	- 0.5
	- 0.6
	- 0.7
	ROI_BOX_HEAD:
	BBOX_REG_WEIGHTS:
	- 10.0
	- 10.0
	- 5.0
	- 5.0
	CLS_AGNOSTIC_BBOX_REG: false
	CONV_DIM: 256
	FC_DIM: 1024
	NAME: FastRCNNConvFCHead
	NORM: ''
	NUM_CONV: 0
	NUM_FC: 2
	POOLER_RESOLUTION: 7
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	SMOOTH_L1_BETA: 0.0
	TRAIN_ON_PRED_BOXES: false
	ROI_HEADS:
	BATCH_SIZE_PER_IMAGE: 512
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	IOU_LABELS:
	- 0
	- 1
	IOU_THRESHOLDS:
	- 0.5
	NAME: StandardROIHeads
	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:
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	- 512
	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: 4
	POOLER_RESOLUTION: 14
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	RPN:
	BATCH_SIZE_PER_IMAGE: 256
	BBOX_REG_WEIGHTS:
	- 1.0
	- 1.0
	- 1.0
	- 1.0
	BOUNDARY_THRESH: -1
	HEAD_NAME: StandardRPNHead
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	- p6
	IOU_LABELS:
	- 0
	- -1
	- 1
	IOU_THRESHOLDS:
	- 0.3
	- 0.7
	LOSS_WEIGHT: 1.0
	NMS_THRESH: 0.7
	POSITIVE_FRACTION: 0.5
	POST_NMS_TOPK_TEST: 1000
	POST_NMS_TOPK_TRAIN: 1000
	PRE_NMS_TOPK_TEST: 1000
	PRE_NMS_TOPK_TRAIN: 2000
	SMOOTH_L1_BETA: 0.0
	SEM_SEG_HEAD:
	COMMON_STRIDE: 4
	CONVS_DIM: 128
	IGNORE_VALUE: 255
	IN_FEATURES:
	- p2
	- p3
	- p4
	- p5
	LOSS_WEIGHT: 1.0
	NAME: SemSegFPNHead
	NORM: GN
	NUM_CLASSES: 54
	WEIGHTS: /content/drive/MyDrive/layoutparser/modele/modele3_NP?/model_final.pth
	OUTPUT_DIR: ../outputs/prima/mask_rcnn_R_50_FPN_3x/
	SEED: -1
	SOLVER:
	BASE_LR: 0.00025
	BIAS_LR_FACTOR: 1.0
	CHECKPOINT_PERIOD: 50
	CLIP_GRADIENTS:
	CLIP_TYPE: value
	CLIP_VALUE: 1.0
	ENABLED: false
	NORM_TYPE: 2.0
	GAMMA: 0.1
	IMS_PER_BATCH: 2
	LR_SCHEDULER_NAME: WarmupMultiStepLR
	MAX_ITER: 300
	MOMENTUM: 0.9
	NESTEROV: false
	STEPS:
	- 210000
	- 250000
	WARMUP_FACTOR: 0.001
	WARMUP_ITERS: 1000
	WARMUP_METHOD: linear
	WEIGHT_DECAY: 0.0001
	WEIGHT_DECAY_BIAS: 0.0001
	WEIGHT_DECAY_NORM: 0.0
	TEST:
	AUG:
	ENABLED: false
	FLIP: true
	MAX_SIZE: 4000
	MIN_SIZES:
	- 400
	- 500
	- 600
	- 700
	- 800
	- 900
	- 1000
	- 1100
	- 1200
	DETECTIONS_PER_IMAGE: 100
	EVAL_PERIOD: 0
	EXPECTED_RESULTS: []
	KEYPOINT_OKS_SIGMAS: []
	PRECISE_BN:
	ENABLED: false
	NUM_ITER: 200
	VERSION: 2
	VIS_PERIOD: 0

	[04/19 13:21:20] detectron2 INFO: Running with full config:
	CUDNN_BENCHMARK: False
	DATALOADER:
	ASPECT_RATIO_GROUPING: True
	FILTER_EMPTY_ANNOTATIONS: True
	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: ('modele-val',)
	TRAIN: ('modele-train',)
	GLOBAL:
	HACK: 1.0
	INPUT:
	CROP:
	ENABLED: False
	SIZE: [0.9, 0.9]
	TYPE: relative_range
	FORMAT: BGR
	MASK_FORMAT: polygon
	MAX_SIZE_TEST: 1333
	MAX_SIZE_TRAIN: 1333
	MIN_SIZE_TEST: 800
	MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
	MIN_SIZE_TRAIN_SAMPLING: choice
	RANDOM_FLIP: horizontal
	MODEL:
	ANCHOR_GENERATOR:
	ANGLES: [[-90, 0, 90]]
	ASPECT_RATIOS: [[0.5, 1.0, 2.0]]
	NAME: DefaultAnchorGenerator
	OFFSET: 0.0
	SIZES: [[32], [64], [128], [256], [512]]
	BACKBONE:
	FREEZE_AT: 2
	NAME: build_resnet_fpn_backbone
	DEVICE: cuda
	FPN:
	FUSE_TYPE: sum
	IN_FEATURES: ['res2', 'res3', 'res4', 'res5']
	NORM:
	OUT_CHANNELS: 256
	KEYPOINT_ON: False
	LOAD_PROPOSALS: False
	MASK_ON: True
	META_ARCHITECTURE: GeneralizedRCNN
	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: [103.53, 116.28, 123.675]
	PIXEL_STD: [1.0, 1.0, 1.0]
	PROPOSAL_GENERATOR:
	MIN_SIZE: 0
	NAME: RPN
	RESNETS:
	DEFORM_MODULATED: False
	DEFORM_NUM_GROUPS: 1
	DEFORM_ON_PER_STAGE: [False, False, False, False]
	DEPTH: 50
	NORM: FrozenBN
	NUM_GROUPS: 1
	OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']
	RES2_OUT_CHANNELS: 256
	RES5_DILATION: 1
	STEM_OUT_CHANNELS: 64
	STRIDE_IN_1X1: True
	WIDTH_PER_GROUP: 64
	RETINANET:
	BBOX_REG_LOSS_TYPE: smooth_l1
	BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
	FOCAL_LOSS_ALPHA: 0.25
	FOCAL_LOSS_GAMMA: 2.0
	IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']
	IOU_LABELS: [0, -1, 1]
	IOU_THRESHOLDS: [0.4, 0.5]
	NMS_THRESH_TEST: 0.5
	NORM:
	NUM_CLASSES: 80
	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: ([10.0, 10.0, 5.0, 5.0], [20.0, 20.0, 10.0, 10.0], [30.0, 30.0, 15.0, 15.0])
	IOUS: (0.5, 0.6, 0.7)
	ROI_BOX_HEAD:
	BBOX_REG_LOSS_TYPE: smooth_l1
	BBOX_REG_LOSS_WEIGHT: 1.0
	BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
	CLS_AGNOSTIC_BBOX_REG: False
	CONV_DIM: 256
	FC_DIM: 1024
	NAME: FastRCNNConvFCHead
	NORM:
	NUM_CONV: 0
	NUM_FC: 2
	POOLER_RESOLUTION: 7
	POOLER_SAMPLING_RATIO: 0
	POOLER_TYPE: ROIAlignV2
	SMOOTH_L1_BETA: 0.0
	TRAIN_ON_PRED_BOXES: False
	ROI_HEADS:
	BATCH_SIZE_PER_IMAGE: 512
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
	IOU_LABELS: [0, 1]
	IOU_THRESHOLDS: [0.5]
	NAME: StandardROIHeads
	NMS_THRESH_TEST: 0.5
	NUM_CLASSES: 2
	POSITIVE_FRACTION: 0.25
	PROPOSAL_APPEND_GT: True
	SCORE_THRESH_TEST: 0.05
	ROI_KEYPOINT_HEAD:
	CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)
	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: 4
	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: (1.0, 1.0, 1.0, 1.0)
	BOUNDARY_THRESH: -1
	HEAD_NAME: StandardRPNHead
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']
	IOU_LABELS: [0, -1, 1]
	IOU_THRESHOLDS: [0.3, 0.7]
	LOSS_WEIGHT: 1.0
	NMS_THRESH: 0.7
	POSITIVE_FRACTION: 0.5
	POST_NMS_TOPK_TEST: 1000
	POST_NMS_TOPK_TRAIN: 1000
	PRE_NMS_TOPK_TEST: 1000
	PRE_NMS_TOPK_TRAIN: 2000
	SMOOTH_L1_BETA: 0.0
	SEM_SEG_HEAD:
	COMMON_STRIDE: 4
	CONVS_DIM: 128
	IGNORE_VALUE: 255
	IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
	LOSS_WEIGHT: 1.0
	NAME: SemSegFPNHead
	NORM: GN
	NUM_CLASSES: 54
	WEIGHTS: /content/drive/MyDrive/layoutparser/modele/modele3_NP?/model_final.pth
	OUTPUT_DIR: /content/drive/MyDrive/layoutparser/modele
	SEED: -1
	SOLVER:
	AMP:
	ENABLED: False
	BASE_LR: 0.00025
	BIAS_LR_FACTOR: 1.0
	CHECKPOINT_PERIOD: 50
	CLIP_GRADIENTS:
	CLIP_TYPE: value
	CLIP_VALUE: 1.0
	ENABLED: False
	NORM_TYPE: 2.0
	GAMMA: 0.1
	IMS_PER_BATCH: 2
	LR_SCHEDULER_NAME: WarmupMultiStepLR
	MAX_ITER: 300
	MOMENTUM: 0.9
	NESTEROV: False
	REFERENCE_WORLD_SIZE: 0
	STEPS: (210000, 250000)
	WARMUP_FACTOR: 0.001
	WARMUP_ITERS: 1000
	WARMUP_METHOD: linear
	WEIGHT_DECAY: 0.0001
	WEIGHT_DECAY_BIAS: 0.0001
	WEIGHT_DECAY_NORM: 0.0
	TEST:
	AUG:
	ENABLED: False
	FLIP: True
	MAX_SIZE: 4000
	MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
	DETECTIONS_PER_IMAGE: 100
	EVAL_PERIOD: 0
	EXPECTED_RESULTS: []
	KEYPOINT_OKS_SIGMAS: []
	PRECISE_BN:
	ENABLED: False
	NUM_ITER: 200
	VERSION: 2
	VIS_PERIOD: 0
	[04/19 13:21:20] detectron2 INFO: Full config saved to /content/drive/MyDrive/layoutparser/modele/config.yaml
	[04/19 13:21:20] d2.utils.env INFO: Using a generated random seed 20391353
	[04/19 13:21:23] d2.engine.defaults INFO: Model:
	GeneralizedRCNN(
	(backbone): FPN(
	(fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
	(fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(top_block): LastLevelMaxPool()
	(bottom_up): ResNet(
	(stem): BasicStem(
	(conv1): Conv2d(
	3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	)
	(res2): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv1): Conv2d(
	64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv2): Conv2d(
	64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
	)
	(conv3): Conv2d(
	64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	)
	)
	(res3): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv1): Conv2d(
	256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	(3): BottleneckBlock(
	(conv1): Conv2d(
	512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv2): Conv2d(
	128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
	)
	(conv3): Conv2d(
	128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	)
	)
	(res4): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	(conv1): Conv2d(
	512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(3): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(4): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	(5): BottleneckBlock(
	(conv1): Conv2d(
	1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
	)
	(conv3): Conv2d(
	256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
	)
	)
	)
	(res5): Sequential(
	(0): BottleneckBlock(
	(shortcut): Conv2d(
	1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	(conv1): Conv2d(
	1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	(1): BottleneckBlock(
	(conv1): Conv2d(
	2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	(2): BottleneckBlock(
	(conv1): Conv2d(
	2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv2): Conv2d(
	512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
	)
	(conv3): Conv2d(
	512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
	(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
	)
	)
	)
	)
	)
	(proposal_generator): RPN(
	(rpn_head): StandardRPNHead(
	(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	(objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
	(anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
	)
	(anchor_generator): DefaultAnchorGenerator(
	(cell_anchors): BufferList()
	)
	)
	(roi_heads): StandardROIHeads(
	(box_pooler): ROIPooler(
	(level_poolers): ModuleList(
	(0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
	(1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
	(2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
	(3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
	)
	)
	(box_head): FastRCNNConvFCHead(
	(flatten): Flatten(start_dim=1, end_dim=-1)
	(fc1): Linear(in_features=12544, out_features=1024, bias=True)
	(fc_relu1): ReLU()
	(fc2): Linear(in_features=1024, out_features=1024, bias=True)
	(fc_relu2): ReLU()
	)
	(box_predictor): FastRCNNOutputLayers(
	(cls_score): Linear(in_features=1024, out_features=3, bias=True)
	(bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
	)
	(mask_pooler): ROIPooler(
	(level_poolers): ModuleList(
	(0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)
	(1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)
	(2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
	(3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
	)
	)
	(mask_head): MaskRCNNConvUpsampleHead(
	(mask_fcn1): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn2): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn3): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(mask_fcn4): Conv2d(
	256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
	(activation): ReLU()
	)
	(deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
	(deconv_relu): ReLU()
	(predictor): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))
	)
	)
	)
	[04/19 13:21:23] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip(), RandomRotation(angle=[-90.0, 0.0])]
	[04/19 13:21:23] d2.data.datasets.coco INFO: Loaded 36 images in COCO format from /content/drive/MyDrive/layoutparser/dataset6/train/via_project_19Apr2023_15h0m_coco.json
	[04/19 13:21:23] d2.data.build INFO: Removed 6 images with no usable annotations. 30 images left.
	[04/19 13:21:23] d2.data.build INFO: Distribution of instances among all 2 categories:
	[36m\| category \| #instances \| category \| #instances \|
	\|:----------:\|:-------------\|:----------:\|:-------------\|
	\| \| 89 \| \| 0 \|
	\| \| \| \| \|
	\| total \| 89 \| \| \|[0m
	[04/19 13:21:23] d2.data.build INFO: Using training sampler TrainingSampler
	[04/19 13:21:23] d2.data.common INFO: Serializing 30 elements to byte tensors and concatenating them all ...
	[04/19 13:21:23] d2.data.common INFO: Serialized dataset takes 0.01 MiB
	[04/19 13:21:23] d2.solver.build WARNING: SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored.
	[04/19 13:21:26] fvcore.common.checkpoint INFO: Loading checkpoint from /content/drive/MyDrive/layoutparser/modele/modele3_NP?/model_final.pth
	[04/19 13:21:31] d2.engine.train_loop INFO: Starting training from iteration 0
	[04/19 13:21:59] d2.utils.events INFO: eta: 0:03:57 iter: 19 total_loss: 0.5817 loss_cls: 0.122 loss_box_reg: 0.1813 loss_mask: 0.2043 loss_rpn_cls: 0.01694 loss_rpn_loc: 0.02236 time: 0.8670 data_time: 0.0615 lr: 4.9953e-06 max_mem: 4741M
	[04/19 13:22:16] d2.utils.events INFO: eta: 0:03:36 iter: 39 total_loss: 0.5271 loss_cls: 0.108 loss_box_reg: 0.1928 loss_mask: 0.1966 loss_rpn_cls: 0.01371 loss_rpn_loc: 0.0178 time: 0.8510 data_time: 0.0094 lr: 9.9902e-06 max_mem: 4741M
	[04/19 13:22:25] fvcore.common.checkpoint INFO: Saving checkpoint to /content/drive/MyDrive/layoutparser/modele/model_0000049.pth
	[04/19 13:22:35] d2.utils.events INFO: eta: 0:03:22 iter: 59 total_loss: 0.5328 loss_cls: 0.09943 loss_box_reg: 0.1768 loss_mask: 0.1878 loss_rpn_cls: 0.01652 loss_rpn_loc: 0.02977 time: 0.8703 data_time: 0.0149 lr: 1.4985e-05 max_mem: 4742M
	[04/19 13:22:53] d2.utils.events INFO: eta: 0:03:09 iter: 79 total_loss: 0.5528 loss_cls: 0.1002 loss_box_reg: 0.1706 loss_mask: 0.2053 loss_rpn_cls: 0.01738 loss_rpn_loc: 0.02357 time: 0.8795 data_time: 0.0108 lr: 1.998e-05 max_mem: 4742M
	[04/19 13:23:11] fvcore.common.checkpoint INFO: Saving checkpoint to /content/drive/MyDrive/layoutparser/modele/model_0000099.pth
	[04/19 13:23:13] d2.utils.events INFO: eta: 0:02:53 iter: 99 total_loss: 0.5248 loss_cls: 0.08265 loss_box_reg: 0.1726 loss_mask: 0.1772 loss_rpn_cls: 0.01976 loss_rpn_loc: 0.02078 time: 0.8858 data_time: 0.0114 lr: 2.4975e-05 max_mem: 4742M
	[04/19 13:23:32] d2.utils.events INFO: eta: 0:02:38 iter: 119 total_loss: 0.5286 loss_cls: 0.09827 loss_box_reg: 0.1722 loss_mask: 0.1774 loss_rpn_cls: 0.01788 loss_rpn_loc: 0.0259 time: 0.8971 data_time: 0.0096 lr: 2.997e-05 max_mem: 4742M
	[04/19 13:23:50] d2.utils.events INFO: eta: 0:02:21 iter: 139 total_loss: 0.5629 loss_cls: 0.09456 loss_box_reg: 0.1846 loss_mask: 0.1865 loss_rpn_cls: 0.02039 loss_rpn_loc: 0.02839 time: 0.9012 data_time: 0.0110 lr: 3.4965e-05 max_mem: 4742M
	[04/19 13:23:59] fvcore.common.checkpoint INFO: Saving checkpoint to /content/drive/MyDrive/layoutparser/modele/model_0000149.pth
	[04/19 13:24:10] d2.utils.events INFO: eta: 0:02:04 iter: 159 total_loss: 0.491 loss_cls: 0.09832 loss_box_reg: 0.1694 loss_mask: 0.1691 loss_rpn_cls: 0.008938 loss_rpn_loc: 0.01734 time: 0.9020 data_time: 0.0080 lr: 3.996e-05 max_mem: 4742M
	[04/19 13:24:29] d2.utils.events INFO: eta: 0:01:47 iter: 179 total_loss: 0.4756 loss_cls: 0.08483 loss_box_reg: 0.162 loss_mask: 0.1571 loss_rpn_cls: 0.01482 loss_rpn_loc: 0.03214 time: 0.9094 data_time: 0.0101 lr: 4.4955e-05 max_mem: 4742M
	[04/19 13:24:49] fvcore.common.checkpoint INFO: Saving checkpoint to /content/drive/MyDrive/layoutparser/modele/model_0000199.pth
	[04/19 13:24:50] d2.utils.events INFO: eta: 0:01:30 iter: 199 total_loss: 0.4405 loss_cls: 0.08707 loss_box_reg: 0.1718 loss_mask: 0.1673 loss_rpn_cls: 0.008687 loss_rpn_loc: 0.02504 time: 0.9157 data_time: 0.0107 lr: 4.995e-05 max_mem: 4742M
	[04/19 13:25:09] d2.utils.events INFO: eta: 0:01:12 iter: 219 total_loss: 0.4541 loss_cls: 0.08539 loss_box_reg: 0.1581 loss_mask: 0.1605 loss_rpn_cls: 0.01627 loss_rpn_loc: 0.01755 time: 0.9168 data_time: 0.0112 lr: 5.4945e-05 max_mem: 4742M
	[04/19 13:25:28] d2.utils.events INFO: eta: 0:00:54 iter: 239 total_loss: 0.4896 loss_cls: 0.09352 loss_box_reg: 0.1829 loss_mask: 0.1675 loss_rpn_cls: 0.0139 loss_rpn_loc: 0.02522 time: 0.9196 data_time: 0.0080 lr: 5.994e-05 max_mem: 4742M
	[04/19 13:25:37] fvcore.common.checkpoint INFO: Saving checkpoint to /content/drive/MyDrive/layoutparser/modele/model_0000249.pth
	[04/19 13:25:48] d2.utils.events INFO: eta: 0:00:36 iter: 259 total_loss: 0.4373 loss_cls: 0.06817 loss_box_reg: 0.1526 loss_mask: 0.1634 loss_rpn_cls: 0.0137 loss_rpn_loc: 0.02394 time: 0.9241 data_time: 0.0098 lr: 6.4935e-05 max_mem: 4742M
	[04/19 13:26:08] d2.utils.events INFO: eta: 0:00:18 iter: 279 total_loss: 0.4922 loss_cls: 0.1011 loss_box_reg: 0.1941 loss_mask: 0.1613 loss_rpn_cls: 0.01023 loss_rpn_loc: 0.03586 time: 0.9272 data_time: 0.0080 lr: 6.993e-05 max_mem: 4742M
	[04/19 13:26:28] fvcore.common.checkpoint INFO: Saving checkpoint to /content/drive/MyDrive/layoutparser/modele/model_0000299.pth
	[04/19 13:26:29] fvcore.common.checkpoint INFO: Saving checkpoint to /content/drive/MyDrive/layoutparser/modele/model_final.pth
	[04/19 13:26:31] d2.utils.events INFO: eta: 0:00:00 iter: 299 total_loss: 0.4673 loss_cls: 0.08663 loss_box_reg: 0.178 loss_mask: 0.1653 loss_rpn_cls: 0.006576 loss_rpn_loc: 0.02131 time: 0.9322 data_time: 0.0116 lr: 7.4925e-05 max_mem: 4742M
	[04/19 13:26:31] d2.engine.hooks INFO: Overall training speed: 298 iterations in 0:04:37 (0.9322 s / it)
	[04/19 13:26:31] d2.engine.hooks INFO: Total training time: 0:04:47 (0:00:09 on hooks)