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faster_rcnn_p2_768/20260728_004007/vis_data/20260728_004007.json

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+{"coco/bbox_mAP": 0.288, "coco/bbox_mAP_50": 0.758, "coco/bbox_mAP_75": 0.114, "coco/bbox_mAP_s": 0.286, "coco/bbox_mAP_m": 0.283, "coco/bbox_mAP_l": -1.0, "data_time": 0.0020230916830209587, "time": 0.013788528931446565, "step": 19}
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+{"lr": 4.999999999999989e-05, "data_time": 0.008348088264465332, "loss": 0.11177320092916489, "loss_rpn_cls": 0.0010000880528241397, "loss_rpn_bbox": 0.006078161625191569, "loss_cls": 0.0324703910574317, "acc": 99.169921875, "loss_bbox": 0.0722245592623949, "time": 0.09261982440948487, "epoch": 20, "iter": 6204, "memory": 3530, "step": 6204}
+{"lr": 4.999999999999989e-05, "data_time": 0.00865373134613037, "loss": 0.11320056848227977, "loss_rpn_cls": 0.0013947434351575794, "loss_rpn_bbox": 0.00597057395381853, "loss_cls": 0.03284449376165867, "acc": 98.53515625, "loss_bbox": 0.072990757599473, "time": 0.0932984447479248, "epoch": 20, "iter": 6254, "memory": 3530, "step": 6254}
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+{"coco/bbox_mAP": 0.289, "coco/bbox_mAP_50": 0.757, "coco/bbox_mAP_75": 0.114, "coco/bbox_mAP_s": 0.288, "coco/bbox_mAP_m": 0.277, "coco/bbox_mAP_l": -1.0, "data_time": 0.0018652300549368572, "time": 0.013562297413491796, "step": 20}

faster_rcnn_p2_768/20260728_004007/vis_data/config.py

@@ -0,0 +1,366 @@
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron2/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            103.53,
+            116.28,
+            123.675,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    neck=dict(
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        num_outs=5,
+        out_channels=256,
+        type='FPN'),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(
+                target_means=[
+                    0.0,
+                    0.0,
+                    0.0,
+                    0.0,
+                ],
+                target_stds=[
+                    0.1,
+                    0.1,
+                    0.2,
+                    0.2,
+                ],
+                type='DeltaXYWHBBoxCoder'),
+            fc_out_channels=1024,
+            in_channels=256,
+            loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+            loss_cls=dict(
+                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+            num_classes=1,
+            reg_class_agnostic=False,
+            roi_feat_size=7,
+            type='Shared2FCBBoxHead'),
+        bbox_roi_extractor=dict(
+            featmap_strides=[
+                4,
+                8,
+                16,
+                32,
+            ],
+            out_channels=256,
+            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
+            type='SingleRoIExtractor'),
+        type='StandardRoIHead'),
+    rpn_head=dict(
+        anchor_generator=dict(
+            ratios=[
+                0.5,
+                1.0,
+                2.0,
+            ],
+            scales=[
+                8,
+            ],
+            strides=[
+                4,
+                8,
+                16,
+                32,
+                64,
+            ],
+            type='AnchorGenerator'),
+        bbox_coder=dict(
+            target_means=[
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            target_stds=[
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            type='DeltaXYWHBBoxCoder'),
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+        loss_cls=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        type='RPNHead'),
+    test_cfg=dict(
+        rcnn=dict(
+            max_per_img=100,
+            nms=dict(iou_threshold=0.5, type='nms'),
+            score_thr=0.05),
+        rpn=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=1000)),
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=False,
+                min_pos_iou=0.5,
+                neg_iou_thr=0.5,
+                pos_iou_thr=0.5,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=True,
+                neg_pos_ub=-1,
+                num=512,
+                pos_fraction=0.25,
+                type='RandomSampler')),
+        rpn=dict(
+            allowed_border=-1,
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=True,
+                min_pos_iou=0.3,
+                neg_iou_thr=0.3,
+                pos_iou_thr=0.7,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=False,
+                neg_pos_ub=-1,
+                num=256,
+                pos_fraction=0.5,
+                type='RandomSampler')),
+        rpn_proposal=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=2000)),
+    type='FasterRCNN')
+optim_wrapper = dict(
+    optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
+    type='OptimWrapper')
+p2_variant = dict(
+    detail_lr_mult=1.0,
+    feature_strides=[
+        4,
+        8,
+        16,
+        32,
+        64,
+    ],
+    image_size=768,
+    name='faster_rcnn_p2_768',
+    pahr=False)
+param_scheduler = [
+    dict(
+        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=20,
+        gamma=0.1,
+        milestones=[
+            13,
+            18,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=20, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768'

faster_rcnn_p2_768/20260728_004007/vis_data/scalars.json

@@ -0,0 +1,140 @@
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+oid sha256:2f7bba4ff884b6d01901b6cb83294b39d3cdf8205426f67f083a9d293f325603
+size 331478839

faster_rcnn_p2_768/last_checkpoint

@@ -0,0 +1 @@
+/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/epoch_20.pth

faster_rcnn_p2_768/patched_config.py

@@ -0,0 +1,366 @@
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron2/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            103.53,
+            116.28,
+            123.675,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    neck=dict(
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        num_outs=5,
+        out_channels=256,
+        type='FPN'),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(
+                target_means=[
+                    0.0,
+                    0.0,
+                    0.0,
+                    0.0,
+                ],
+                target_stds=[
+                    0.1,
+                    0.1,
+                    0.2,
+                    0.2,
+                ],
+                type='DeltaXYWHBBoxCoder'),
+            fc_out_channels=1024,
+            in_channels=256,
+            loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+            loss_cls=dict(
+                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+            num_classes=1,
+            reg_class_agnostic=False,
+            roi_feat_size=7,
+            type='Shared2FCBBoxHead'),
+        bbox_roi_extractor=dict(
+            featmap_strides=[
+                4,
+                8,
+                16,
+                32,
+            ],
+            out_channels=256,
+            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
+            type='SingleRoIExtractor'),
+        type='StandardRoIHead'),
+    rpn_head=dict(
+        anchor_generator=dict(
+            ratios=[
+                0.5,
+                1.0,
+                2.0,
+            ],
+            scales=[
+                8,
+            ],
+            strides=[
+                4,
+                8,
+                16,
+                32,
+                64,
+            ],
+            type='AnchorGenerator'),
+        bbox_coder=dict(
+            target_means=[
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            target_stds=[
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            type='DeltaXYWHBBoxCoder'),
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+        loss_cls=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        type='RPNHead'),
+    test_cfg=dict(
+        rcnn=dict(
+            max_per_img=100,
+            nms=dict(iou_threshold=0.5, type='nms'),
+            score_thr=0.05),
+        rpn=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=1000)),
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=False,
+                min_pos_iou=0.5,
+                neg_iou_thr=0.5,
+                pos_iou_thr=0.5,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=True,
+                neg_pos_ub=-1,
+                num=512,
+                pos_fraction=0.25,
+                type='RandomSampler')),
+        rpn=dict(
+            allowed_border=-1,
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=True,
+                min_pos_iou=0.3,
+                neg_iou_thr=0.3,
+                pos_iou_thr=0.7,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=False,
+                neg_pos_ub=-1,
+                num=256,
+                pos_fraction=0.5,
+                type='RandomSampler')),
+        rpn_proposal=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=2000)),
+    type='FasterRCNN')
+optim_wrapper = dict(
+    optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
+    type='OptimWrapper')
+p2_variant = dict(
+    detail_lr_mult=1.0,
+    feature_strides=[
+        4,
+        8,
+        16,
+        32,
+        64,
+    ],
+    image_size=768,
+    name='faster_rcnn_p2_768',
+    pahr=False)
+param_scheduler = [
+    dict(
+        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=20,
+        gamma=0.1,
+        milestones=[
+            13,
+            18,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=20, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768'

faster_rcnn_p2_768/test_results/20260728_005318/20260728_005318.json

@@ -0,0 +1 @@
+{"coco/bbox_mAP": 0.229, "coco/bbox_mAP_50": 0.645, "coco/bbox_mAP_75": 0.078, "coco/bbox_mAP_s": 0.227, "coco/bbox_mAP_m": 0.341, "coco/bbox_mAP_l": -1.0, "data_time": 0.0023052831218667224, "time": 0.015438783658693915}

faster_rcnn_p2_768/test_results/20260728_005318/20260728_005318.log

@@ -0,0 +1,512 @@
+2026/07/28 00:53:19 - mmengine - INFO - 
+------------------------------------------------------------
+System environment:
+    sys.platform: linux
+    Python: 3.11.15 (main, Jul 18 2026, 17:02:49) [Clang 22.1.3 ]
+    CUDA available: True
+    MUSA available: False
+    numpy_random_seed: 42
+    GPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition
+    CUDA_HOME: /usr/local/cuda
+    NVCC: Cuda compilation tools, release 13.1, V13.1.115
+    GCC: cc (Debian 14.2.0-19) 14.2.0
+    PyTorch: 2.13.0+cu130
+    PyTorch compiling details: PyTorch built with:
+  - GCC 13.3
+  - C++ Version: 202002
+  - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications
+  - Intel(R) MKL-DNN v3.12.0 (Git Hash 80afa71049cd69a3df32adcccb623b12cd7baa22)
+  - OpenMP 201511 (a.k.a. OpenMP 4.5)
+  - LAPACK is enabled (usually provided by MKL)
+  - NNPACK is enabled
+  - CPU capability usage: AVX512
+  - CUDA Runtime 13.0
+  - NVCC architecture flags: -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;-gencode;arch=compute_100,code=sm_100;-gencode;arch=compute_120,code=sm_120
+  - CuDNN 92.0  (built against CUDA 13.2)
+  - Magma 2.6.1
+  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=cf30153c4c131c8164ee7798e5022d810682e2cb, CUDA_FLAGS= -DLIBCUDACXX_ENABLE_SIMPLIFIED_COMPLEX_OPERATIONS -Xfatbin -compress-all -DONNX_NAMESPACE=onnx_torch -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 -gencode arch=compute_100,code=sm_100 -gencode arch=compute_120,code=sm_120 -Xcudafe --diag_suppress=cc_clobber_ignored,--diag_suppress=field_without_dll_interface,--diag_suppress=base_class_has_different_dll_interface,--diag_suppress=dll_interface_conflict_none_assumed,--diag_suppress=dll_interface_conflict_dllexport_assumed,--diag_suppress=bad_friend_decl --expt-relaxed-constexpr --expt-extended-lambda -Xfatbin -compress-all --threads 2 -compress-mode=size -Wno-deprecated-gpu-targets --expt-extended-lambda -DCUB_WRAPPED_NAMESPACE=at_cuda_detail -DDISABLE_CUSPARSE_DEPRECATED -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -DC10_NODEPRECATED, CUDA_VERSION=13.0, CUDNN_VERSION=9.20.0, CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DHAS_CUPTI -DUSE_FBGEMM -DUSE_MSLK -DUSE_PYTORCH_QNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference -Wno-error=dangling-reference -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.13.0, USE_CUDA=1, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, 
+
+    TorchVision: 0.28.0+cu130
+    OpenCV: 4.11.0
+    MMEngine: 0.10.7
+
+Runtime environment:
+    cudnn_benchmark: False
+    dist_cfg: {'backend': 'nccl'}
+    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
+    seed: 42
+    Distributed launcher: none
+    Distributed training: False
+    GPU number: 1
+------------------------------------------------------------
+
+2026/07/28 00:53:19 - mmengine - INFO - Config:
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/best_coco_bbox_mAP_75_epoch_7.pth'
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron2/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            103.53,
+            116.28,
+            123.675,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    neck=dict(
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        num_outs=5,
+        out_channels=256,
+        type='FPN'),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(
+                target_means=[
+                    0.0,
+                    0.0,
+                    0.0,
+                    0.0,
+                ],
+                target_stds=[
+                    0.1,
+                    0.1,
+                    0.2,
+                    0.2,
+                ],
+                type='DeltaXYWHBBoxCoder'),
+            fc_out_channels=1024,
+            in_channels=256,
+            loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+            loss_cls=dict(
+                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+            num_classes=1,
+            reg_class_agnostic=False,
+            roi_feat_size=7,
+            type='Shared2FCBBoxHead'),
+        bbox_roi_extractor=dict(
+            featmap_strides=[
+                4,
+                8,
+                16,
+                32,
+            ],
+            out_channels=256,
+            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
+            type='SingleRoIExtractor'),
+        type='StandardRoIHead'),
+    rpn_head=dict(
+        anchor_generator=dict(
+            ratios=[
+                0.5,
+                1.0,
+                2.0,
+            ],
+            scales=[
+                8,
+            ],
+            strides=[
+                4,
+                8,
+                16,
+                32,
+                64,
+            ],
+            type='AnchorGenerator'),
+        bbox_coder=dict(
+            target_means=[
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            target_stds=[
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            type='DeltaXYWHBBoxCoder'),
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+        loss_cls=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        type='RPNHead'),
+    test_cfg=dict(
+        rcnn=dict(
+            max_per_img=100,
+            nms=dict(iou_threshold=0.5, type='nms'),
+            score_thr=0.05),
+        rpn=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=1000)),
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=False,
+                min_pos_iou=0.5,
+                neg_iou_thr=0.5,
+                pos_iou_thr=0.5,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=True,
+                neg_pos_ub=-1,
+                num=512,
+                pos_fraction=0.25,
+                type='RandomSampler')),
+        rpn=dict(
+            allowed_border=-1,
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=True,
+                min_pos_iou=0.3,
+                neg_iou_thr=0.3,
+                pos_iou_thr=0.7,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=False,
+                neg_pos_ub=-1,
+                num=256,
+                pos_fraction=0.5,
+                type='RandomSampler')),
+        rpn_proposal=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=2000)),
+    type='FasterRCNN')
+optim_wrapper = dict(
+    optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
+    type='OptimWrapper')
+p2_variant = dict(
+    detail_lr_mult=1.0,
+    feature_strides=[
+        4,
+        8,
+        16,
+        32,
+        64,
+    ],
+    image_size=768,
+    name='faster_rcnn_p2_768',
+    pahr=False)
+param_scheduler = [
+    dict(
+        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=20,
+        gamma=0.1,
+        milestones=[
+            13,
+            18,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=20, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/test_results'
+
+2026/07/28 00:53:20 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
+2026/07/28 00:53:20 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DetVisualizationHook               
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DetVisualizationHook               
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2026/07/28 00:53:20 - mmengine - WARNING - The prefix is not set in metric class DumpDetResults.
+2026/07/28 00:53:20 - mmengine - INFO - Load checkpoint from /marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/best_coco_bbox_mAP_75_epoch_7.pth
+2026/07/28 00:53:22 - mmengine - INFO - Epoch(test) [ 50/584]    eta: 0:00:19  time: 0.0373  data_time: 0.0075  memory: 390  
+2026/07/28 00:53:23 - mmengine - INFO - Epoch(test) [100/584]    eta: 0:00:12  time: 0.0141  data_time: 0.0021  memory: 390  
+2026/07/28 00:53:23 - mmengine - INFO - Epoch(test) [150/584]    eta: 0:00:09  time: 0.0137  data_time: 0.0019  memory: 390  
+2026/07/28 00:53:24 - mmengine - INFO - Epoch(test) [200/584]    eta: 0:00:07  time: 0.0136  data_time: 0.0020  memory: 390  
+2026/07/28 00:53:25 - mmengine - INFO - Epoch(test) [250/584]    eta: 0:00:06  time: 0.0136  data_time: 0.0021  memory: 390  
+2026/07/28 00:53:25 - mmengine - INFO - Epoch(test) [300/584]    eta: 0:00:04  time: 0.0132  data_time: 0.0019  memory: 390  
+2026/07/28 00:53:26 - mmengine - INFO - Epoch(test) [350/584]    eta: 0:00:03  time: 0.0130  data_time: 0.0014  memory: 390  
+2026/07/28 00:53:27 - mmengine - INFO - Epoch(test) [400/584]    eta: 0:00:03  time: 0.0133  data_time: 0.0019  memory: 390  
+2026/07/28 00:53:27 - mmengine - INFO - Epoch(test) [450/584]    eta: 0:00:02  time: 0.0137  data_time: 0.0019  memory: 390  
+2026/07/28 00:53:28 - mmengine - INFO - Epoch(test) [500/584]    eta: 0:00:01  time: 0.0128  data_time: 0.0017  memory: 390  
+2026/07/28 00:53:29 - mmengine - INFO - Epoch(test) [550/584]    eta: 0:00:00  time: 0.0134  data_time: 0.0016  memory: 390  
+2026/07/28 00:53:29 - mmengine - INFO - Evaluating bbox...
+2026/07/28 00:53:29 - mmengine - INFO - bbox_mAP_copypaste: 0.229 0.645 0.078 0.227 0.341 -1.000
+2026/07/28 00:53:29 - mmengine - INFO - Results has been saved to /marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/test_results/predictions.pkl.
+2026/07/28 00:53:29 - mmengine - INFO - Epoch(test) [584/584]    coco/bbox_mAP: 0.2290  coco/bbox_mAP_50: 0.6450  coco/bbox_mAP_75: 0.0780  coco/bbox_mAP_s: 0.2270  coco/bbox_mAP_m: 0.3410  coco/bbox_mAP_l: -1.0000  data_time: 0.0023  time: 0.0154

faster_rcnn_p2_768/test_results/20260728_005318/vis_data/config.py

@@ -0,0 +1,366 @@
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/best_coco_bbox_mAP_75_epoch_7.pth'
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron2/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            103.53,
+            116.28,
+            123.675,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    neck=dict(
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        num_outs=5,
+        out_channels=256,
+        type='FPN'),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(
+                target_means=[
+                    0.0,
+                    0.0,
+                    0.0,
+                    0.0,
+                ],
+                target_stds=[
+                    0.1,
+                    0.1,
+                    0.2,
+                    0.2,
+                ],
+                type='DeltaXYWHBBoxCoder'),
+            fc_out_channels=1024,
+            in_channels=256,
+            loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+            loss_cls=dict(
+                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+            num_classes=1,
+            reg_class_agnostic=False,
+            roi_feat_size=7,
+            type='Shared2FCBBoxHead'),
+        bbox_roi_extractor=dict(
+            featmap_strides=[
+                4,
+                8,
+                16,
+                32,
+            ],
+            out_channels=256,
+            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
+            type='SingleRoIExtractor'),
+        type='StandardRoIHead'),
+    rpn_head=dict(
+        anchor_generator=dict(
+            ratios=[
+                0.5,
+                1.0,
+                2.0,
+            ],
+            scales=[
+                8,
+            ],
+            strides=[
+                4,
+                8,
+                16,
+                32,
+                64,
+            ],
+            type='AnchorGenerator'),
+        bbox_coder=dict(
+            target_means=[
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            target_stds=[
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            type='DeltaXYWHBBoxCoder'),
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+        loss_cls=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        type='RPNHead'),
+    test_cfg=dict(
+        rcnn=dict(
+            max_per_img=100,
+            nms=dict(iou_threshold=0.5, type='nms'),
+            score_thr=0.05),
+        rpn=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=1000)),
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=False,
+                min_pos_iou=0.5,
+                neg_iou_thr=0.5,
+                pos_iou_thr=0.5,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=True,
+                neg_pos_ub=-1,
+                num=512,
+                pos_fraction=0.25,
+                type='RandomSampler')),
+        rpn=dict(
+            allowed_border=-1,
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=True,
+                min_pos_iou=0.3,
+                neg_iou_thr=0.3,
+                pos_iou_thr=0.7,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=False,
+                neg_pos_ub=-1,
+                num=256,
+                pos_fraction=0.5,
+                type='RandomSampler')),
+        rpn_proposal=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=2000)),
+    type='FasterRCNN')
+optim_wrapper = dict(
+    optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
+    type='OptimWrapper')
+p2_variant = dict(
+    detail_lr_mult=1.0,
+    feature_strides=[
+        4,
+        8,
+        16,
+        32,
+        64,
+    ],
+    image_size=768,
+    name='faster_rcnn_p2_768',
+    pahr=False)
+param_scheduler = [
+    dict(
+        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=20,
+        gamma=0.1,
+        milestones=[
+            13,
+            18,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=20, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/test_results'

faster_rcnn_p2_768/test_results/p2_summary.json

@@ -0,0 +1,28 @@
+{
+  "variant": "faster_rcnn_p2_768",
+  "validation": {
+    "coco/bbox_mAP": null,
+    "coco/bbox_mAP_50": null,
+    "coco/bbox_mAP_75": null
+  },
+  "test": {
+    "coco/bbox_mAP": 0.229,
+    "coco/bbox_mAP_50": 0.645,
+    "coco/bbox_mAP_75": 0.078,
+    "coco/bbox_mAP_s": 0.227,
+    "coco/bbox_mAP_m": 0.341,
+    "coco/bbox_mAP_l": -1.0,
+    "data_time": 0.0023052831218667224,
+    "time": 0.015438783658693915
+  },
+  "tiny": {
+    "tiny_gt": 105,
+    "tiny_recall_50": 0.5523809523809524,
+    "tiny_recall_75": 0.0761904761904762,
+    "tiny_center_error_mean_px": 1.604177713394165,
+    "tiny_center_error_median_px": 1.4855691194534302
+  },
+  "correction": null,
+  "test_latency_seconds": 15.04216360099963,
+  "test_latency_ms_per_image": 25.75712945376649
+}

faster_rcnn_p2_768/test_results/patched_config.py

@@ -0,0 +1,366 @@
+auto_scale_lr = dict(base_batch_size=16, enable=True)
+backend_args = None
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1,
+        max_keep_ckpts=1,
+        rule='greater',
+        save_best='coco/bbox_mAP_75',
+        save_last=True,
+        type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/best_coco_bbox_mAP_75_epoch_7.pth'
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=50,
+        frozen_stages=1,
+        init_cfg=dict(
+            checkpoint='open-mmlab://detectron2/resnet50_caffe',
+            type='Pretrained'),
+        norm_cfg=dict(requires_grad=False, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='caffe',
+        type='ResNet'),
+    data_preprocessor=dict(
+        bgr_to_rgb=False,
+        mean=[
+            103.53,
+            116.28,
+            123.675,
+        ],
+        pad_size_divisor=32,
+        std=[
+            1.0,
+            1.0,
+            1.0,
+        ],
+        type='DetDataPreprocessor'),
+    neck=dict(
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        num_outs=5,
+        out_channels=256,
+        type='FPN'),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(
+                target_means=[
+                    0.0,
+                    0.0,
+                    0.0,
+                    0.0,
+                ],
+                target_stds=[
+                    0.1,
+                    0.1,
+                    0.2,
+                    0.2,
+                ],
+                type='DeltaXYWHBBoxCoder'),
+            fc_out_channels=1024,
+            in_channels=256,
+            loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+            loss_cls=dict(
+                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+            num_classes=1,
+            reg_class_agnostic=False,
+            roi_feat_size=7,
+            type='Shared2FCBBoxHead'),
+        bbox_roi_extractor=dict(
+            featmap_strides=[
+                4,
+                8,
+                16,
+                32,
+            ],
+            out_channels=256,
+            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
+            type='SingleRoIExtractor'),
+        type='StandardRoIHead'),
+    rpn_head=dict(
+        anchor_generator=dict(
+            ratios=[
+                0.5,
+                1.0,
+                2.0,
+            ],
+            scales=[
+                8,
+            ],
+            strides=[
+                4,
+                8,
+                16,
+                32,
+                64,
+            ],
+            type='AnchorGenerator'),
+        bbox_coder=dict(
+            target_means=[
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            target_stds=[
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            type='DeltaXYWHBBoxCoder'),
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+        loss_cls=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        type='RPNHead'),
+    test_cfg=dict(
+        rcnn=dict(
+            max_per_img=100,
+            nms=dict(iou_threshold=0.5, type='nms'),
+            score_thr=0.05),
+        rpn=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=1000)),
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=False,
+                min_pos_iou=0.5,
+                neg_iou_thr=0.5,
+                pos_iou_thr=0.5,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=True,
+                neg_pos_ub=-1,
+                num=512,
+                pos_fraction=0.25,
+                type='RandomSampler')),
+        rpn=dict(
+            allowed_border=-1,
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=True,
+                min_pos_iou=0.3,
+                neg_iou_thr=0.3,
+                pos_iou_thr=0.7,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=False,
+                neg_pos_ub=-1,
+                num=256,
+                pos_fraction=0.5,
+                type='RandomSampler')),
+        rpn_proposal=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=2000)),
+    type='FasterRCNN')
+optim_wrapper = dict(
+    optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
+    type='OptimWrapper')
+p2_variant = dict(
+    detail_lr_mult=1.0,
+    feature_strides=[
+        4,
+        8,
+        16,
+        32,
+        64,
+    ],
+    image_size=768,
+    name='faster_rcnn_p2_768',
+    pahr=False)
+param_scheduler = [
+    dict(
+        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=20,
+        gamma=0.1,
+        milestones=[
+            13,
+            18,
+        ],
+        type='MultiStepLR'),
+]
+randomness = dict(seed=42)
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=20, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/train.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file=
+        '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+        backend_args=None,
+        data_prefix=dict(img='/marimo/LevirShipData/All Images/'),
+        data_root='',
+        metainfo=dict(classes=('ship', )),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                768,
+                768,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '/marimo/mmdet_code/mmdetection/data/levir_ship_coco/annotations/val.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = '/marimo/mmdet_code/mmdetection/work_dirs/levir_faster_rcnn_p2/faster_rcnn_p2_768/test_results'

faster_rcnn_p2_768/test_results/predictions.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4dff2d1c6b3a6de60d64da372e4738228a32294f0e0fad515b023ac06c00b4a
+size 831236