Model is not detecting anything correctly

[yenchingp]

Hi all, I am trying to use Faster RCNN on the SKU110K, dense object dataset, detect objects. There is only one class ('object'). I have converted my data to Coco format but I am not getting any results

DONE (t=0.03s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.001
11/12 06:08:39 - mmengine - INFO - bbox_mAP_copypaste: 0.000 0.000 0.000 -1.000 0.000 0.000
11/12 06:08:39 - mmengine - INFO - Iter(val) [5/5]    coco/bbox_mAP: 0.0000  coco/bbox_mAP_50: 0.0000  coco/bbox_mAP_75: 0.0000  coco/bbox_mAP_s: -1.0000  coco/bbox_mAP_m: 0.0000  coco/bbox_mAP_l: 0.0000  data_time: 0.0386  time: 10.7459

here is my config output:

System environment:
    sys.platform: linux
    Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
    CUDA available: False
    numpy_random_seed: 1625607648
    GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    PyTorch: 2.1.0+cu118
    PyTorch compiling details: 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 v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - 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 -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 -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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=2.1.0, 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, 

    TorchVision: 0.16.0+cu118
    OpenCV: 4.8.0
    MMEngine: 0.9.1

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 1625607648
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

11/12 06:04:19 - mmengine - INFO - Config:
auto_scale_lr = dict(base_batch_size=16, enable=False)
backend_args = None
classes = ('object', )
data_root = '/content/mmdetection/data/coco/'
dataset_type = 'CocoDataset'
default_hooks = dict(
    checkpoint=dict(by_epoch=False, interval=10000, 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=False, type='LogProcessor', window_size=50)
loss_cls = dict(
    alpha=0.25, gamma=2.0, loss_weight=1.0, type='FocalLoss', use_sigmoid=True)
model = dict(
    backbone=dict(
        depth=50,
        frozen_stages=1,
        init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'),
        norm_cfg=dict(requires_grad=True, type='BN'),
        norm_eval=True,
        num_stages=4,
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        style='pytorch',
        type='ResNet'),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_size_divisor=32,
        std=[
            58.395,
            57.12,
            57.375,
        ],
        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_thr=0.5, type='nms'),
            score_thr=0.05),
        rpn=dict(
            max_per_img=3000,
            min_bbox_size=0,
            nms=dict(iou_threshold=0.5, type='nms'),
            nms_across_levels=False,
            nms_post=3000,
            nms_pre=3000)),
    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=0.5,
                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=3000,
            min_bbox_size=0,
            nms=dict(iou_threshold=0.5, type='nms'),
            nms_across_levels=False,
            nms_post=3000,
            nms_pre=5000)),
    type='FasterRCNN')
optim_wrapper = dict(
    clip_grad=None,
    optimizer=dict(lr=0.002, momentum=0.9, type='SGD', weight_decay=0.0001),
    type='OptimWrapper')
param_scheduler = [
    dict(begin=0, by_epoch=True, end=500, start_factor=0.001, type='LinearLR'),
    dict(
        begin=1,
        by_epoch=True,
        end=10,
        gamma=0.1,
        milestones=[
            6,
            8,
        ],
        type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='/content/mmdetection/data/coco/coco_annotations_test.json',
        backend_args=None,
        data_prefix=dict(img='images/test/'),
        data_root='/content/mmdetection/data/coco/',
        metainfo=dict(classes=('object', )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                800,
                1333,
            ), type='Resize'),
            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=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    ann_file='/content/mmdetection/data/coco/coco_annotations_test.json',
    backend_args=None,
    format_only=True,
    metric=[
        'bbox',
    ],
    outfile_prefix='./work_dirs/coco_detection/test',
    type='CocoMetric')
test_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        800,
        1333,
    ), type='Resize'),
    dict(
        meta_keys=(
            'img_id',
            'img_path',
            'ori_shape',
            'img_shape',
            'scale_factor',
        ),
        type='PackDetInputs'),
]
train_cfg = dict(max_iters=10, type='IterBasedTrainLoop', val_interval=1)
train_dataloader = dict(
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    batch_size=2,
    dataset=dict(
        ann_file='coco_annotations_train.json',
        backend_args=None,
        data_prefix=dict(img='images/train/'),
        data_root='/content/mmdetection/data/coco/',
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        metainfo=dict(classes=('object', )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(keep_ratio=True, scale=(
                800,
                1333,
            ), type='Resize'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PackDetInputs'),
        ],
        type='CocoDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(keep_ratio=True, scale=(
        800,
        1333,
    ), 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='coco_annotations_val.json',
        backend_args=None,
        data_prefix=dict(img='images/val/'),
        data_root='/content/mmdetection/data/coco/',
        metainfo=dict(classes=('object', )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                800,
                1333,
            ), type='Resize'),
            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=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    ann_file='/content/mmdetection/data/coco/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 = './work_dirs/faster_rcnn_resnet50'

11/12 06:04:21 - 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.
11/12 06:04:21 - 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                         
 -------------------- 
loading annotations into memory...
Done (t=0.08s)
creating index...
index created!
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
11/12 06:04:23 - mmengine - INFO - load model from: torchvision://resnet50
11/12 06:04:23 - mmengine - INFO - Loads checkpoint by torchvision backend from path: torchvision://resnet50
11/12 06:04:23 - mmengine - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: fc.weight, fc.bias

11/12 06:04:24 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
11/12 06:04:24 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
11/12 06:04:24 - mmengine - INFO - Checkpoints will be saved to /content/mmdetection/work_dirs/faster_rcnn_resnet50.
11/12 06:06:31 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.38s).
Accumulating evaluation results...
DONE (t=0.01s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.001
11/12 06:06:32 - mmengine - INFO - bbox_mAP_copypaste: 0.000 0.000 0.000 -1.000 0.000 0.000
11/12 06:06:32 - mmengine - INFO - Iter(val) [5/5]    coco/bbox_mAP: 0.0000  coco/bbox_mAP_50: 0.0000  coco/bbox_mAP_75: 0.0000  coco/bbox_mAP_s: -1.0000  coco/bbox_mAP_m: 0.0000  coco/bbox_mAP_l: 0.0000  data_time: 0.1145  time: 12.1187
11/12 06:08:37 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=2.44s).
Accumulating evaluation results...
DONE (t=0.03s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.001
11/12 06:08:39 - mmengine - INFO - bbox_mAP_copypaste: 0.000 0.000 0.000 -1.000 0.000 0.000
11/12 06:08:39 - mmengine - INFO - Iter(val) [5/5]    coco/bbox_mAP: 0.0000  coco/bbox_mAP_50: 0.0000  coco/bbox_mAP_75: 0.0000  coco/bbox_mAP_s: -1.0000  coco/bbox_mAP_m: 0.0000  coco/bbox_mAP_l: 0.0000  data_time: 0.0386  time: 10.7459
11/12 06:10:31 - mmengine - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.18s).
Accumulating evaluation results...
DONE (t=0.01s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.001
11/12 06:10:33 - mmengine - INFO - bbox_mAP_copypaste: 0.000 0.000 0.000 -1.000 0.000 0.000
11/12 06:10:33 - mmengine - INFO - Iter(val) [5/5]    coco/bbox_mAP: 0.0000  coco/bbox_mAP_50: 0.0000  coco/bbox_mAP_75: 0.0000  coco/bbox_mAP_s: -1.0000  coco/bbox_mAP_m: 0.0000  coco/bbox_mAP_l: 0.0000  data_time: 0.0370  time: 9.9783

[ccomkhj]

1. Check if your train loss decreases
   -> if not, Try with lower lr=0.0002
2. Check if your coco (valid) is correct (id, ...)