| {"mmdet_version": "2.19.12f84a48", "CLASSES": ["airplane", "antelope", "bear", "bicycle", "bird", "bus", "car", "cattle", "dog", "domestic_cat", "elephant", "fox", "giant_panda", "hamster", "horse", "lion", "lizard", "monkey", "motorcycle", "rabbit", "red_panda", "sheep", "snake", "squirrel", "tiger", "train", "turtle", "watercraft", "whale", "zebra"], "env_info": "sys.platform: linux\nPython: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3: NVIDIA A100-SXM4-40GB\nCUDA_HOME: /usr/local/cuda-12.0\nNVCC: Build cuda_12.0.r12.0/compiler.31968024_0\nGCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nPyTorch: 1.10.1\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - 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_61,code=sm_61;-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_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.1, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.11.2\nOpenCV: 4.9.0\nMMCV: 1.3.17\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMDetection: 2.19.1+2f84a48", "config": "model = dict(\n detector=dict(\n type='FasterRCNN',\n backbone=dict(\n type='ResNet',\n depth=101,\n num_stages=4,\n out_indices=(3, ),\n strides=(1, 2, 2, 1),\n dilations=(1, 1, 1, 2),\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=True),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(\n type='Pretrained', checkpoint='torchvision://resnet101')),\n neck=dict(\n type='ChannelMapper',\n in_channels=[2048],\n out_channels=512,\n kernel_size=3),\n rpn_head=dict(\n type='RPNHead',\n in_channels=512,\n feat_channels=512,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[4, 8, 16, 32],\n ratios=[0.5, 1.0, 2.0],\n strides=[16]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(\n type='SmoothL1Loss', beta=0.1111111111111111,\n loss_weight=1.0)),\n roi_head=dict(\n type='MambaRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(\n type='RoIAlign', output_size=7, sampling_ratio=2),\n out_channels=512,\n featmap_strides=[16]),\n bbox_head=dict(\n type='MambaBBoxHead',\n in_channels=512,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=30,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.2, 0.2, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss',\n use_sigmoid=False,\n loss_weight=1.0),\n loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0),\n num_shared_fcs=2,\n topk=75,\n aggregator=dict(\n type='MambaAggregator',\n in_channels=1024,\n num_attention_blocks=16))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=0,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=6000,\n max_per_img=600,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=6000,\n max_per_img=300,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.0001,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100))),\n type='MAMBA')\ndataset_type = 'ImagenetVIDDataset'\ndata_root = 'data/ILSVRC/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadMultiImagesFromFile'),\n dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),\n dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),\n dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),\n dict(\n type='SeqNormalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='SeqPad', size_divisor=16),\n dict(\n type='VideoCollect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_instance_ids']),\n dict(type='ConcatVideoReferences'),\n dict(type='SeqDefaultFormatBundle', ref_prefix='ref')\n]\ntest_pipeline = [\n dict(type='LoadMultiImagesFromFile'),\n dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),\n dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),\n dict(\n type='SeqNormalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='SeqPad', size_divisor=16),\n dict(\n type='VideoCollect',\n keys=['img'],\n meta_keys=('num_left_ref_imgs', 'frame_stride')),\n dict(type='ConcatVideoReferences'),\n dict(type='MultiImagesToTensor', ref_prefix='ref'),\n dict(type='ToList')\n]\ndata = dict(\n samples_per_gpu=1,\n workers_per_gpu=4,\n train=[\n dict(\n type='ImagenetVIDDataset',\n ann_file='data/ILSVRC/annotations/imagenet_vid_train.json',\n img_prefix='data/ILSVRC/Data/VID',\n ref_img_sampler=dict(\n num_ref_imgs=2,\n frame_range=1000,\n filter_key_img=True,\n method='bilateral_uniform'),\n pipeline=[\n dict(type='LoadMultiImagesFromFile'),\n dict(\n type='SeqLoadAnnotations', with_bbox=True,\n with_track=True),\n dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),\n dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),\n dict(\n type='SeqNormalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='SeqPad', size_divisor=16),\n dict(\n type='VideoCollect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_instance_ids']),\n dict(type='ConcatVideoReferences'),\n dict(type='SeqDefaultFormatBundle', ref_prefix='ref')\n ]),\n dict(\n type='ImagenetVIDDataset',\n load_as_video=False,\n ann_file='data/ILSVRC/annotations/imagenet_det_30plus1cls.json',\n img_prefix='data/ILSVRC/Data/DET',\n ref_img_sampler=dict(\n num_ref_imgs=2,\n frame_range=0,\n filter_key_img=False,\n method='bilateral_uniform'),\n pipeline=[\n dict(type='LoadMultiImagesFromFile'),\n dict(\n type='SeqLoadAnnotations', with_bbox=True,\n with_track=True),\n dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),\n dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),\n dict(\n type='SeqNormalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='SeqPad', size_divisor=16),\n dict(\n type='VideoCollect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_instance_ids']),\n dict(type='ConcatVideoReferences'),\n dict(type='SeqDefaultFormatBundle', ref_prefix='ref')\n ])\n ],\n val=dict(\n type='ImagenetVIDDataset',\n ann_file='data/ILSVRC/annotations/imagenet_vid_val.json',\n img_prefix='data/ILSVRC/Data/VID',\n ref_img_sampler=dict(\n num_ref_imgs=14,\n frame_range=[-7, 7],\n stride=1,\n method='test_with_adaptive_stride'),\n pipeline=[\n dict(type='LoadMultiImagesFromFile'),\n dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),\n dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),\n dict(\n type='SeqNormalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='SeqPad', size_divisor=16),\n dict(\n type='VideoCollect',\n keys=['img'],\n meta_keys=('num_left_ref_imgs', 'frame_stride')),\n dict(type='ConcatVideoReferences'),\n dict(type='MultiImagesToTensor', ref_prefix='ref'),\n dict(type='ToList')\n ],\n test_mode=True,\n shuffle_video_frames=True),\n test=dict(\n type='ImagenetVIDDataset',\n ann_file='data/ILSVRC/annotations/imagenet_vid_val.json',\n img_prefix='data/ILSVRC/Data/VID',\n ref_img_sampler=dict(\n num_ref_imgs=14,\n frame_range=[-7, 7],\n stride=1,\n method='test_with_adaptive_stride'),\n pipeline=[\n dict(type='LoadMultiImagesFromFile'),\n dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),\n dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),\n dict(\n type='SeqNormalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='SeqPad', size_divisor=16),\n dict(\n type='VideoCollect',\n keys=['img'],\n meta_keys=('num_left_ref_imgs', 'frame_stride')),\n dict(type='ConcatVideoReferences'),\n dict(type='MultiImagesToTensor', ref_prefix='ref'),\n dict(type='ToList')\n ],\n test_mode=True,\n shuffle_video_frames=True))\ncheckpoint_config = dict(interval=3)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\noptimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=0.3333333333333333,\n step=[4])\nrunner = dict(type='EpochBasedRunner', max_epochs=6)\nis_video_model = True\ntotal_epochs = 6\nevaluation = dict(metric=['bbox'], vid_style=True, interval=6)\nwork_dir = './work_dirs/mamba_r101_dc5_6x'\ngpu_ids = range(0, 4)\n", "seed": 1485688156, "exp_name": "mamba_r101_dc5_6x.py", "epoch": 3, "iter": 82266, "mmcv_version": "1.3.17", "time": "Sat Jan 27 00:20:35 2024"} | |
| {"mode": "train", "epoch": 4, "iter": 50, "lr": 0.001, "memory": 5240, "data_time": 2.25163, "loss_rpn_cls": 0.01472, "loss_rpn_bbox": 0.01577, "loss_cls": 0.09827, "acc": 96.19318, "loss_bbox": 0.03768, "loss": 0.16645, "grad_norm": 3.07772, "time": 2.42548} | |
| {"mode": "train", "epoch": 4, "iter": 100, "lr": 0.001, "memory": 5240, "data_time": 0.0132, "loss_rpn_cls": 0.01464, "loss_rpn_bbox": 0.01331, "loss_cls": 0.09769, "acc": 96.08969, "loss_bbox": 0.03562, "loss": 0.16127, "grad_norm": 2.9773, "time": 0.16137} | |
| {"mode": "train", "epoch": 4, "iter": 150, "lr": 0.001, "memory": 5240, "data_time": 0.01417, "loss_rpn_cls": 0.0152, "loss_rpn_bbox": 0.01445, "loss_cls": 0.08985, "acc": 96.41257, "loss_bbox": 0.03395, "loss": 0.15345, "grad_norm": 2.89385, "time": 0.16129} | |
| {"mode": "train", "epoch": 4, "iter": 200, "lr": 0.001, "memory": 5240, "data_time": 0.01431, "loss_rpn_cls": 0.01276, "loss_rpn_bbox": 0.01324, "loss_cls": 0.08643, "acc": 96.61226, "loss_bbox": 0.03161, "loss": 0.14405, "grad_norm": 2.79605, "time": 0.16402} | |
| {"mode": "train", "epoch": 4, "iter": 250, "lr": 0.001, "memory": 5240, "data_time": 0.01453, "loss_rpn_cls": 0.01463, "loss_rpn_bbox": 0.01336, "loss_cls": 0.08379, "acc": 96.75098, "loss_bbox": 0.03161, "loss": 0.14339, "grad_norm": 2.9684, "time": 0.16312} | |
| {"mode": "train", "epoch": 4, "iter": 300, "lr": 0.001, "memory": 5240, "data_time": 0.01489, "loss_rpn_cls": 0.01437, "loss_rpn_bbox": 0.01487, "loss_cls": 0.08984, "acc": 96.48177, "loss_bbox": 0.03696, "loss": 0.15604, "grad_norm": 2.84406, "time": 0.16614} | |
| {"mode": "train", "epoch": 4, "iter": 350, "lr": 0.001, "memory": 5240, "data_time": 0.01507, "loss_rpn_cls": 0.0132, "loss_rpn_bbox": 0.01513, "loss_cls": 0.09287, "acc": 96.26001, "loss_bbox": 0.03627, "loss": 0.15746, "grad_norm": 3.11204, "time": 0.16315} | |
| {"mode": "train", "epoch": 4, "iter": 400, "lr": 0.001, "memory": 5240, "data_time": 0.01478, "loss_rpn_cls": 0.01282, "loss_rpn_bbox": 0.01398, "loss_cls": 0.08275, "acc": 96.6215, "loss_bbox": 0.03228, "loss": 0.14183, "grad_norm": 2.7156, "time": 0.16924} | |
| {"mode": "train", "epoch": 4, "iter": 450, "lr": 0.001, "memory": 5240, "data_time": 0.01423, "loss_rpn_cls": 0.01435, "loss_rpn_bbox": 0.01535, "loss_cls": 0.0761, "acc": 96.91426, "loss_bbox": 0.03091, "loss": 0.13672, "grad_norm": 2.78119, "time": 0.16623} | |
| {"mode": "train", "epoch": 4, "iter": 500, "lr": 0.001, "memory": 5240, "data_time": 0.0152, "loss_rpn_cls": 0.0133, "loss_rpn_bbox": 0.01446, "loss_cls": 0.08254, "acc": 96.82897, "loss_bbox": 0.03326, "loss": 0.14356, "grad_norm": 2.85384, "time": 0.16338} | |
| {"mode": "train", "epoch": 4, "iter": 550, "lr": 0.001, "memory": 5240, "data_time": 0.01455, "loss_rpn_cls": 0.01322, "loss_rpn_bbox": 0.01456, "loss_cls": 0.0799, "acc": 96.773, "loss_bbox": 0.02997, "loss": 0.13765, "grad_norm": 2.80348, "time": 0.16413} | |
| {"mode": "train", "epoch": 4, "iter": 600, "lr": 0.001, "memory": 5240, "data_time": 0.01423, "loss_rpn_cls": 0.01308, "loss_rpn_bbox": 0.01374, "loss_cls": 0.08099, "acc": 96.71971, "loss_bbox": 0.03062, "loss": 0.13843, "grad_norm": 2.80511, "time": 0.15863} | |
| {"mode": "train", "epoch": 4, "iter": 650, "lr": 0.001, "memory": 5240, "data_time": 0.0157, "loss_rpn_cls": 0.01401, "loss_rpn_bbox": 0.01462, "loss_cls": 0.07932, "acc": 96.82197, "loss_bbox": 0.03196, "loss": 0.13991, "grad_norm": 2.72845, "time": 0.16108} | |
| {"mode": "train", "epoch": 4, "iter": 700, "lr": 0.001, "memory": 5240, "data_time": 0.01506, "loss_rpn_cls": 0.01537, "loss_rpn_bbox": 0.01539, "loss_cls": 0.08516, "acc": 96.63674, "loss_bbox": 0.03088, "loss": 0.14679, "grad_norm": 2.93706, "time": 0.16969} | |
| {"mode": "train", "epoch": 4, "iter": 750, "lr": 0.001, "memory": 5240, "data_time": 0.0155, "loss_rpn_cls": 0.01183, "loss_rpn_bbox": 0.01558, "loss_cls": 0.08614, "acc": 96.56251, "loss_bbox": 0.03383, "loss": 0.14739, "grad_norm": 2.8024, "time": 0.16213} | |
| {"mode": "train", "epoch": 4, "iter": 800, "lr": 0.001, "memory": 5240, "data_time": 0.01439, "loss_rpn_cls": 0.01399, "loss_rpn_bbox": 0.01434, "loss_cls": 0.08284, "acc": 96.71298, "loss_bbox": 0.03212, "loss": 0.14329, "grad_norm": 2.95164, "time": 0.15998} | |
| {"mode": "train", "epoch": 4, "iter": 850, "lr": 0.001, "memory": 5240, "data_time": 0.01457, "loss_rpn_cls": 0.01343, "loss_rpn_bbox": 0.01416, "loss_cls": 0.07872, "acc": 96.87927, "loss_bbox": 0.03041, "loss": 0.13671, "grad_norm": 2.9297, "time": 0.16137} | |
| {"mode": "train", "epoch": 4, "iter": 900, "lr": 0.001, "memory": 5240, "data_time": 0.01541, "loss_rpn_cls": 0.01228, "loss_rpn_bbox": 0.01416, "loss_cls": 0.07621, "acc": 97.09513, "loss_bbox": 0.03012, "loss": 0.13277, "grad_norm": 2.58152, "time": 0.16573} | |
| {"mode": "train", "epoch": 4, "iter": 950, "lr": 0.001, "memory": 5240, "data_time": 0.01571, "loss_rpn_cls": 0.01401, "loss_rpn_bbox": 0.0148, "loss_cls": 0.07692, "acc": 96.89204, "loss_bbox": 0.03008, "loss": 0.13581, "grad_norm": 2.71987, "time": 0.16087} | |
| {"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.001, "memory": 5240, "data_time": 0.01526, "loss_rpn_cls": 0.01191, "loss_rpn_bbox": 0.01535, "loss_cls": 0.07293, "acc": 97.12939, "loss_bbox": 0.03011, "loss": 0.1303, "grad_norm": 2.7068, "time": 0.17154} | |
| {"mode": "train", "epoch": 4, "iter": 1050, "lr": 0.001, "memory": 5240, "data_time": 0.0151, "loss_rpn_cls": 0.0132, "loss_rpn_bbox": 0.01252, "loss_cls": 0.07332, "acc": 97.115, "loss_bbox": 0.03172, "loss": 0.13076, "grad_norm": 2.62633, "time": 0.16225} | |
| {"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.001, "memory": 5240, "data_time": 0.01453, "loss_rpn_cls": 0.01425, "loss_rpn_bbox": 0.01514, "loss_cls": 0.07624, "acc": 97.04771, "loss_bbox": 0.03268, "loss": 0.13831, "grad_norm": 2.64062, "time": 0.15913} | |
| {"mode": "train", "epoch": 4, "iter": 1150, "lr": 0.001, "memory": 5240, "data_time": 0.01541, "loss_rpn_cls": 0.01457, "loss_rpn_bbox": 0.01505, "loss_cls": 0.07415, "acc": 97.02579, "loss_bbox": 0.02943, "loss": 0.13319, "grad_norm": 2.58482, "time": 0.16196} | |
| {"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.001, "memory": 5240, "data_time": 0.0154, "loss_rpn_cls": 0.0125, "loss_rpn_bbox": 0.01301, "loss_cls": 0.07135, "acc": 97.17308, "loss_bbox": 0.02838, "loss": 0.12524, "grad_norm": 2.65778, "time": 0.15857} | |
| {"mode": "train", "epoch": 4, "iter": 1250, "lr": 0.001, "memory": 5240, "data_time": 0.01556, "loss_rpn_cls": 0.01617, "loss_rpn_bbox": 0.01605, "loss_cls": 0.07496, "acc": 97.09345, "loss_bbox": 0.03203, "loss": 0.1392, "grad_norm": 2.79609, "time": 0.15986} | |
| {"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.001, "memory": 5240, "data_time": 0.01578, "loss_rpn_cls": 0.01538, "loss_rpn_bbox": 0.01344, "loss_cls": 0.07164, "acc": 97.19482, "loss_bbox": 0.02913, "loss": 0.12959, "grad_norm": 2.74879, "time": 0.16043} | |
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| {"mode": "val", "epoch": 6, "iter": 176126, "lr": 0.0001, "all": 0.83816, "fast": 0.65287, "medium": 0.83817, "slow": 0.89507, "airplane": 0.9333, "antelope": 0.84372, "bear": 0.94015, "bicycle": 0.78247, "bird": 0.76559, "bus": 0.81857, "car": 0.67295, "cattle": 0.84021, "dog": 0.86293, "domestic_cat": 0.92887, "elephant": 0.85516, "fox": 0.95413, "giant_panda": 0.86743, "hamster": 0.98857, "horse": 0.81364, "lion": 0.8219, "lizard": 0.86128, "monkey": 0.63969, "motorcycle": 0.88081, "rabbit": 0.86796, "red_panda": 0.89088, "sheep": 0.6786, "snake": 0.81145, "squirrel": 0.7049, "tiger": 0.92197, "train": 0.87632, "turtle": 0.86328, "watercraft": 0.69932, "whale": 0.78162, "zebra": 0.97703} | |