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open-mmlab / mmtracking

OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
https://mmtracking.readthedocs.io/en/latest/
Apache License 2.0
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The validation result is lower than the training accuracy using selsa model for own dataset #371

Open lantudou opened 2 years ago

lantudou commented 2 years ago

Hi, I want to train the selsa model for my own dataset. A sample of my customized dataset json is given below: {"categories": [{"id": 1, "name": "car"}], "images": [{"id": 1, "video_id": 1, "file_name": "1_1.jpg", "height": 480, "width": 560, "frame_id": 0, "is_vid_train_frame": true}, {"id": 2, "video_id": 1, "file_name": "1_2.jpg", "height": 480, "width": 560, "frame_id": 1, "is_vid_train_frame": true}, {"id": 3, "video_id": 1, "file_name": "1_3.jpg", "height": 480, "width": 560, "frame_id": 2, "is_vid_train_frame": true}, {"id": 4, "video_id": 1, "file_name": "1_4.jpg", "height": 480, "width": 560, "frame_id": 3, "is_vid_train_frame": true}, {"id": 5, "video_id": 1, "file_name": "1_5.jpg", "height": 480, "width": 560, "frame_id": 4, "is_vid_train_frame": true}, {"id": 6, "video_id": 1, "file_name": "1_6.jpg", "height": 480, "width": 560, "frame_id": 5, "is_vid_train_frame": true}, {"id": 7, "video_id": 1, "file_name": "1_7.jpg", "height": 480, "width": 560, "frame_id": 6, "is_vid_train_frame": true}, {"id": 8, "video_id": 1, "file_name": "1_8.jpg", "height": 480, "width": 560, "frame_id": 7, "is_vid_train_frame": true}, {"id": 9, "video_id": 1, "file_name": "1_9.jpg", "height": 480, "width": 560, "frame_id": 8, "is_vid_train_frame": true}, {"id": 10, "video_id": 1, "file_name": "1_10.jpg", "height": 480, "width": 560, "frame_id": 9, "is_vid_train_frame": true}, {"id": 11, "video_id": 1, "file_name": "1_11.jpg", "height": 480, "width": 560, "frame_id": 10, "is_vid_train_frame": true}, {"id": 12, "video_id": 1, "file_name": "1_12.jpg", "height": 480, "width": 560, "frame_id": 11, "is_vid_train_frame": true}, {"id": 13, "video_id": 1, "file_name": "1_13.jpg", "height": 480, "width": 560, "frame_id": 12, "is_vid_train_frame": true}, {"id": 14, "video_id": 1, "file_name": "1_14.jpg", "height": 480, "width": 560, "frame_id": 13, "is_vid_train_frame": true}, {"id": 15, "video_id": 1, "file_name": "1_15.jpg", "height": 480, "width": 560, "frame_id": 14, "is_vid_train_frame": true}, {"id": 16, "video_id": 1, "file_name": "1_16.jpg", "height": 480, "width": 560, "frame_id": 15, "is_vid_train_frame": true}, {"id": 17, "video_id": 1, "file_name": "1_17.jpg", "height": 480, "width": 560, "frame_id": 16, "is_vid_train_frame": true}, {"id": 18, "video_id": 1, "file_name": "1_18.jpg", "height": 480, "width": 560, "frame_id": 17, "is_vid_train_frame": true}, {"id": 19, "video_id": 1, "file_name": "1_19.jpg", "height": 480, "width": 560, "frame_id": 18, "is_vid_train_frame": true}, {"id": 20, "video_id": 1, "file_name": "1_20.jpg", "height": 480, "width": 560, "frame_id": 19, "is_vid_train_frame": true}, {"id": 21, "video_id": 1, "file_name": "1_21.jpg", "height": 480, "width": 560, "frame_id": 20, "is_vid_train_frame": true}, {"id": 22, "video_id": 1, "file_name": "1_22.jpg", "height": 480, "width": 560, "frame_id": 21, "is_vid_train_frame": true}, {"id": 23, "video_id": 1, "file_name": "1_23.jpg", "height": 480, "width": 560, "frame_id": 22, "is_vid_train_frame": true}, {"id": 24, "video_id": 1, "file_name": "1_24.jpg", "height": 480, "width": 560, "frame_id": 23, "is_vid_train_frame": true}, {"id": 25, "video_id": 1, "file_name": "1_25.jpg", "height": 480, "width": 560, "frame_id": 24, "is_vid_train_frame": true}, {"id": 26, "video_id": 1, "file_name": "1_26.jpg", "height": 480, "width": 560, "frame_id": 25, "is_vid_train_frame": true}, {"id": 27, "video_id": 1, "file_name": "1_27.jpg", "height": 480, "width": 560, "frame_id": 26, "is_vid_train_frame": true}, {"id": 28, "video_id": 1, "file_name": "1_28.jpg", "height": 480, "width": 560, "frame_id": 27, "is_vid_train_frame": true}, {"id": 29, "video_id": 1, "file_name": "1_29.jpg", "height": 480, "width": 560, "frame_id": 28, "is_vid_train_frame": true}, {"id": 30, "video_id": 1, "file_name": "1_30.jpg", "height": 480, "width": 560, "frame_id": 29, "is_vid_train_frame": true}, {"id": 31, "video_id": 1, "file_name": "1_31.jpg", "height": 480, "width": 560, "frame_id": 30, "is_vid_train_frame": true}, {"id": 32, "video_id": 1, "file_name": 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"is_vid_train_frame": true, "visibility": 1.0}, {"id": 222, "image_id": 412, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [299, 183, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 223, "image_id": 413, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [298, 183, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 224, "image_id": 414, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [302, 180, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 225, "image_id": 415, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [301, 176, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 226, "image_id": 416, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [305, 176, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 227, "image_id": 417, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [306, 174, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 228, "image_id": 418, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [307, 173, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 229, "image_id": 419, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [311, 175, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 230, "image_id": 420, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [312, 173, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 231, "image_id": 421, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [314, 174, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 232, "image_id": 422, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [316, 173, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}, {"id": 233, "image_id": 423, "video_id": 2, "instance_id": 1, "category_id": 1, "bbox": [320, 173, 85, 147], "area": 12495.0, "occluded": false, "truncated": false, "iscrowd": false, "ignore": false, "is_vid_train_frame": true, "visibility": 1.0}], "videos": [{"id": 1, "name": "1"}, {"id": 2, "name": "2"}]}

lantudou commented 2 years ago

And here is my selsa configuration, only for test I changed the validation dataset as the same as training dataset.

model = dict(
    detector=dict(
        type='FasterRCNN',
        backbone=dict(
            type='ResNet',
            depth=50,
            num_stages=4,
            out_indices=(3, ),
            strides=(1, 2, 2, 1),
            dilations=(1, 1, 1, 2),
            frozen_stages=1,
            norm_cfg=dict(type='BN', requires_grad=True),
            norm_eval=True,
            style='pytorch',
            init_cfg=dict(
                type='Pretrained', checkpoint='torchvision://resnet50')),
        neck=dict(
            type='ChannelMapper',
            in_channels=[2048],
            out_channels=512,
            kernel_size=3),
        rpn_head=dict(
            type='RPNHead',
            in_channels=512,
            feat_channels=512,
            anchor_generator=dict(
                type='AnchorGenerator',
                scales=[4, 8, 16, 32],
                ratios=[0.5, 1.0, 2.0],
                strides=[16]),
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[1.0, 1.0, 1.0, 1.0]),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
            loss_bbox=dict(
                type='SmoothL1Loss', beta=0.1111111111111111,
                loss_weight=1.0)),
        roi_head=dict(
            type='SelsaRoIHead',
            bbox_roi_extractor=dict(
                type='TemporalRoIAlign',
                roi_layer=dict(
                    type='RoIAlign', output_size=7, sampling_ratio=2),
                out_channels=512,
                featmap_strides=[16],
                num_most_similar_points=2,
                num_temporal_attention_blocks=4),
            bbox_head=dict(
                type='SelsaBBoxHead',
                in_channels=512,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=1,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.2, 0.2, 0.2, 0.2]),
                reg_class_agnostic=False,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0),
                num_shared_fcs=3,
                aggregator=dict(
                    type='SelsaAggregator',
                    in_channels=1024,
                    num_attention_blocks=16))),
        train_cfg=dict(
            rpn=dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.3,
                    min_pos_iou=0.3,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=256,
                    pos_fraction=0.5,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=False),
                allowed_border=0,
                pos_weight=-1,
                debug=False),
            rpn_proposal=dict(
                nms_pre=6000,
                max_per_img=600,
                nms=dict(type='nms', iou_threshold=0.7),
                min_bbox_size=0),
            rcnn=dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=256,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False)),
        test_cfg=dict(
            rpn=dict(
                nms_pre=6000,
                max_per_img=300,
                nms=dict(type='nms', iou_threshold=0.7),
                min_bbox_size=0),
            rcnn=dict(
                score_thr=0.0001,
                nms=dict(type='nms', iou_threshold=0.5),
                max_per_img=100))),
    type='SELSA')
dataset_type = 'CocoVideoDataset'
data_root = 'data/ILSVRC/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadMultiImagesFromFile'),
    dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),
    dict(type='SeqResize', img_scale=(600, 600), keep_ratio=True),
    dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
    dict(
        type='SeqNormalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='SeqPad', size_divisor=16),
    dict(
        type='VideoCollect',
        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_instance_ids']),
    dict(type='ConcatVideoReferences'),
    dict(type='SeqDefaultFormatBundle', ref_prefix='ref')
]
test_pipeline = [
    dict(type='LoadMultiImagesFromFile'),
    dict(type='SeqResize', img_scale=(600, 600), keep_ratio=True),
    dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),
    dict(
        type='SeqNormalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='SeqPad', size_divisor=16),
    dict(
        type='VideoCollect',
        keys=['img'],
        meta_keys=('num_left_ref_imgs', 'frame_stride')),
    dict(type='ConcatVideoReferences'),
    dict(type='MultiImagesToTensor', ref_prefix='ref'),
    dict(type='ToList')
]
data = dict(
    samples_per_gpu=1,
    workers_per_gpu=2,
    train=dict(
        type='CocoVideoDataset',
        ann_file='/share_data/YuhaoSun/TrainValid/train_coco.json',
        img_prefix='/share_data/YuhaoSun/TrainValid/Images_copy/',
        classes=('car', ),
        ref_img_sampler=dict(
            num_ref_imgs=14,
            frame_range=[-7, 7],
            filter_key_img=False,
            method='uniform'),
        pipeline=[
            dict(type='LoadMultiImagesFromFile'),
            dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),
            dict(type='SeqResize', img_scale=(600, 600), keep_ratio=True),
            dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
            dict(
                type='SeqNormalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='SeqPad', size_divisor=16),
            dict(
                type='VideoCollect',
                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_instance_ids']),
            dict(type='ConcatVideoReferences'),
            dict(type='SeqDefaultFormatBundle', ref_prefix='ref')
        ]),
    val=dict(
        type='CocoVideoDataset',
        ann_file='/share_data/YuhaoSun/TrainValid/train_coco.json',
        img_prefix='/share_data/YuhaoSun/TrainValid/Images_copy/',
        ref_img_sampler=dict(
            num_ref_imgs=14,
            frame_range=[-7, 7],
            filter_key_img=False,
            method='test_with_adaptive_stride'),
        pipeline=[
            dict(type='LoadMultiImagesFromFile'),
            dict(type='SeqResize', img_scale=(600, 600), keep_ratio=True),
            dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),
            dict(
                type='SeqNormalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='SeqPad', size_divisor=16),
            dict(
                type='VideoCollect',
                keys=['img'],
                meta_keys=('num_left_ref_imgs', 'frame_stride')),
            dict(type='ConcatVideoReferences'),
            dict(type='MultiImagesToTensor', ref_prefix='ref'),
            dict(type='ToList')
        ],
        test_mode=True,
        classes=('car', )),
    test=dict(
        type='CocoVideoDataset',
        ann_file='/share_data/YuhaoSun/TrainValid/train_coco.json',
        img_prefix='/share_data/YuhaoSun/TrainValid/Images_copy/',
        ref_img_sampler=dict(
            num_ref_imgs=14,
            frame_range=[-7, 7],
            filter_key_img=False,
            method='test_with_adaptive_stride'),
        pipeline=[
            dict(type='LoadMultiImagesFromFile'),
            dict(type='SeqResize', img_scale=(600, 600), keep_ratio=True),
            dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),
            dict(
                type='SeqNormalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='SeqPad', size_divisor=16),
            dict(
                type='VideoCollect',
                keys=['img'],
                meta_keys=('num_left_ref_imgs', 'frame_stride')),
            dict(type='ConcatVideoReferences'),
            dict(type='MultiImagesToTensor', ref_prefix='ref'),
            dict(type='ToList')
        ],
        test_mode=True,
        classes=('car', )))
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
train_data_root = '/share_data/YuhaoSun/TrainValid'
test_data_root = '/share_data/YuhaoSun/Test'
classes = ('car', )
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.3333333333333333,
    step=[2, 5])
total_epochs = 7
evaluation = dict(metric=['bbox'], interval=4)
work_dir = './work_dirs/selsa_troialign_faster_rcnn_r50_dc5_7e_imagenetvid'
gpu_ids = range(0, 1)

However, during the training process, I got the high accuracy but low mAP in evaluation. what is wrong with my configuration?


2021-12-15 07:36:19,796 - mmtrack - INFO - Epoch [1][50/783]    lr: 3.987e-03, eta: 1:21:58, time: 0.906, data_time: 0.068, memory: 10834, loss_rpn_cls: 0.3306, loss_rpn_bbox: 0.0168, loss_cls: 0.2117, acc: 96.8426, loss_bbox: 0.0852, loss: 0.6443, grad_norm: 6.7095
2021-12-15 07:37:02,211 - mmtrack - INFO - Epoch [1][100/783]   lr: 4.653e-03, eta: 1:18:39, time: 0.848, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0965, loss_rpn_bbox: 0.0312, loss_cls: 0.1226, acc: 96.6719, loss_bbox: 0.0514, loss: 0.3017, grad_norm: 3.1169
2021-12-15 07:37:44,165 - mmtrack - INFO - Epoch [1][150/783]   lr: 5.320e-03, eta: 1:16:47, time: 0.839, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0757, loss_rpn_bbox: 0.0091, loss_cls: 0.1340, acc: 96.7279, loss_bbox: 0.0421, loss: 0.2609, grad_norm: 2.6552
2021-12-15 07:38:25,711 - mmtrack - INFO - Epoch [1][200/783]   lr: 5.987e-03, eta: 1:15:20, time: 0.831, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.1218, loss_rpn_bbox: 0.0668, loss_cls: 0.1577, acc: 96.5137, loss_bbox: 0.0547, loss: 0.4010, grad_norm: 4.4360
2021-12-15 07:39:08,801 - mmtrack - INFO - Epoch [1][250/783]   lr: 6.653e-03, eta: 1:14:43, time: 0.862, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0834, loss_rpn_bbox: 0.0287, loss_cls: 0.1884, acc: 95.1397, loss_bbox: 0.0796, loss: 0.3801, grad_norm: 3.6525
2021-12-15 07:39:49,901 - mmtrack - INFO - Epoch [1][300/783]   lr: 7.320e-03, eta: 1:13:30, time: 0.822, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0949, loss_rpn_bbox: 0.0335, loss_cls: 0.1583, acc: 95.9908, loss_bbox: 0.0576, loss: 0.3443, grad_norm: 2.3676
2021-12-15 07:40:32,360 - mmtrack - INFO - Epoch [1][350/783]   lr: 7.987e-03, eta: 1:12:46, time: 0.849, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0940, loss_rpn_bbox: 0.0344, loss_cls: 0.1476, acc: 95.6853, loss_bbox: 0.0620, loss: 0.3379, grad_norm: 3.3197
2021-12-15 07:41:13,436 - mmtrack - INFO - Epoch [1][400/783]   lr: 8.653e-03, eta: 1:11:45, time: 0.822, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0655, loss_rpn_bbox: 0.0139, loss_cls: 0.1333, acc: 96.1395, loss_bbox: 0.0525, loss: 0.2652, grad_norm: 2.2907
2021-12-15 07:41:57,320 - mmtrack - INFO - Epoch [1][450/783]   lr: 9.320e-03, eta: 1:11:19, time: 0.878, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0824, loss_rpn_bbox: 0.0284, loss_cls: 0.1124, acc: 96.3516, loss_bbox: 0.0457, loss: 0.2689, grad_norm: 1.6045
2021-12-15 07:42:40,115 - mmtrack - INFO - Epoch [1][500/783]   lr: 9.987e-03, eta: 1:10:39, time: 0.856, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0862, loss_rpn_bbox: 0.0253, loss_cls: 0.1175, acc: 95.5845, loss_bbox: 0.0567, loss: 0.2857, grad_norm: 2.0306
2021-12-15 07:43:22,854 - mmtrack - INFO - Epoch [1][550/783]   lr: 1.000e-02, eta: 1:09:58, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0907, loss_rpn_bbox: 0.0214, loss_cls: 0.1169, acc: 95.4894, loss_bbox: 0.0572, loss: 0.2862, grad_norm: 1.8056
2021-12-15 07:44:05,080 - mmtrack - INFO - Epoch [1][600/783]   lr: 1.000e-02, eta: 1:09:13, time: 0.845, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0520, loss_rpn_bbox: 0.0090, loss_cls: 0.1293, acc: 95.1306, loss_bbox: 0.0582, loss: 0.2486, grad_norm: 1.5817
2021-12-15 07:44:48,318 - mmtrack - INFO - Epoch [1][650/783]   lr: 1.000e-02, eta: 1:08:36, time: 0.865, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0947, loss_rpn_bbox: 0.0213, loss_cls: 0.0967, acc: 95.9810, loss_bbox: 0.0470, loss: 0.2597, grad_norm: 1.7823
2021-12-15 07:45:31,874 - mmtrack - INFO - Epoch [1][700/783]   lr: 1.000e-02, eta: 1:07:59, time: 0.871, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0712, loss_rpn_bbox: 0.0221, loss_cls: 0.0858, acc: 96.5312, loss_bbox: 0.0386, loss: 0.2177, grad_norm: 1.2808
2021-12-15 07:46:14,822 - mmtrack - INFO - Epoch [1][750/783]   lr: 1.000e-02, eta: 1:07:19, time: 0.859, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0933, loss_rpn_bbox: 0.0255, loss_cls: 0.1092, acc: 95.8087, loss_bbox: 0.0538, loss: 0.2818, grad_norm: 1.8194
2021-12-15 07:46:43,565 - mmtrack - INFO - Saving checkpoint at 1 epochs
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
2021-12-15 07:47:31,096 - mmtrack - INFO - Epoch [2][50/783]    lr: 1.000e-02, eta: 1:03:42, time: 0.894, data_time: 0.067, memory: 10834, loss_rpn_cls: 0.0776, loss_rpn_bbox: 0.0190, loss_cls: 0.1401, acc: 95.6291, loss_bbox: 0.0632, loss: 0.2998, grad_norm: 1.9659
2021-12-15 07:48:13,558 - mmtrack - INFO - Epoch [2][100/783]   lr: 1.000e-02, eta: 1:03:08, time: 0.849, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0811, loss_rpn_bbox: 0.0150, loss_cls: 0.0916, acc: 96.8281, loss_bbox: 0.0368, loss: 0.2245, grad_norm: 1.2564
2021-12-15 07:48:56,107 - mmtrack - INFO - Epoch [2][150/783]   lr: 1.000e-02, eta: 1:02:33, time: 0.851, data_time: 0.014, memory: 10834, loss_rpn_cls: 0.0680, loss_rpn_bbox: 0.0128, loss_cls: 0.0903, acc: 96.2891, loss_bbox: 0.0501, loss: 0.2212, grad_norm: 1.1886
2021-12-15 07:49:38,440 - mmtrack - INFO - Epoch [2][200/783]   lr: 1.000e-02, eta: 1:01:57, time: 0.847, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0608, loss_rpn_bbox: 0.0082, loss_cls: 0.0768, acc: 96.7266, loss_bbox: 0.0413, loss: 0.1872, grad_norm: 1.2856
2021-12-15 07:50:20,694 - mmtrack - INFO - Epoch [2][250/783]   lr: 1.000e-02, eta: 1:01:19, time: 0.845, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0753, loss_rpn_bbox: 0.0181, loss_cls: 0.1056, acc: 95.9122, loss_bbox: 0.0430, loss: 0.2421, grad_norm: 1.7747
2021-12-15 07:51:03,317 - mmtrack - INFO - Epoch [2][300/783]   lr: 1.000e-02, eta: 1:00:43, time: 0.852, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0736, loss_rpn_bbox: 0.0168, loss_cls: 0.0824, acc: 96.6719, loss_bbox: 0.0402, loss: 0.2130, grad_norm: 1.4605
2021-12-15 07:51:45,637 - mmtrack - INFO - Epoch [2][350/783]   lr: 1.000e-02, eta: 1:00:05, time: 0.846, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0482, loss_rpn_bbox: 0.0089, loss_cls: 0.0744, acc: 97.4062, loss_bbox: 0.0352, loss: 0.1666, grad_norm: 1.1594
2021-12-15 07:52:28,376 - mmtrack - INFO - Epoch [2][400/783]   lr: 1.000e-02, eta: 0:59:28, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0392, loss_rpn_bbox: 0.0092, loss_cls: 0.0922, acc: 96.2344, loss_bbox: 0.0427, loss: 0.1833, grad_norm: 1.3498
2021-12-15 07:53:10,863 - mmtrack - INFO - Epoch [2][450/783]   lr: 1.000e-02, eta: 0:58:50, time: 0.850, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.1025, loss_rpn_bbox: 0.0214, loss_cls: 0.1035, acc: 96.3631, loss_bbox: 0.0444, loss: 0.2717, grad_norm: 1.9641
2021-12-15 07:53:53,177 - mmtrack - INFO - Epoch [2][500/783]   lr: 1.000e-02, eta: 0:58:11, time: 0.846, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0567, loss_rpn_bbox: 0.0123, loss_cls: 0.0993, acc: 96.1641, loss_bbox: 0.0411, loss: 0.2094, grad_norm: 1.4441
2021-12-15 07:54:35,626 - mmtrack - INFO - Epoch [2][550/783]   lr: 1.000e-02, eta: 0:57:32, time: 0.849, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0655, loss_rpn_bbox: 0.0213, loss_cls: 0.0834, acc: 97.1875, loss_bbox: 0.0409, loss: 0.2111, grad_norm: 1.6517
2021-12-15 07:55:17,895 - mmtrack - INFO - Epoch [2][600/783]   lr: 1.000e-02, eta: 0:56:52, time: 0.845, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0553, loss_rpn_bbox: 0.0155, loss_cls: 0.0779, acc: 96.8906, loss_bbox: 0.0458, loss: 0.1945, grad_norm: 1.2690
2021-12-15 07:56:00,141 - mmtrack - INFO - Epoch [2][650/783]   lr: 1.000e-02, eta: 0:56:13, time: 0.845, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0608, loss_rpn_bbox: 0.0083, loss_cls: 0.0837, acc: 97.0152, loss_bbox: 0.0377, loss: 0.1905, grad_norm: 1.3081
2021-12-15 07:56:42,491 - mmtrack - INFO - Epoch [2][700/783]   lr: 1.000e-02, eta: 0:55:33, time: 0.847, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0809, loss_rpn_bbox: 0.0189, loss_cls: 0.0807, acc: 96.8750, loss_bbox: 0.0388, loss: 0.2193, grad_norm: 1.4496
2021-12-15 07:57:24,692 - mmtrack - INFO - Epoch [2][750/783]   lr: 1.000e-02, eta: 0:54:52, time: 0.844, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0588, loss_rpn_bbox: 0.0136, loss_cls: 0.0728, acc: 97.1146, loss_bbox: 0.0306, loss: 0.1758, grad_norm: 1.2231
2021-12-15 07:57:52,767 - mmtrack - INFO - Saving checkpoint at 2 epochs
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
2021-12-15 07:58:40,748 - mmtrack - INFO - Epoch [3][50/783]    lr: 1.000e-03, eta: 0:52:46, time: 0.904, data_time: 0.068, memory: 10834, loss_rpn_cls: 0.0451, loss_rpn_bbox: 0.0101, loss_cls: 0.0887, acc: 96.8359, loss_bbox: 0.0390, loss: 0.1829, grad_norm: 1.1982
2021-12-15 07:59:23,482 - mmtrack - INFO - Epoch [3][100/783]   lr: 1.000e-03, eta: 0:52:09, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0362, loss_rpn_bbox: 0.0069, loss_cls: 0.0763, acc: 97.3359, loss_bbox: 0.0361, loss: 0.1556, grad_norm: 1.2760
2021-12-15 08:00:06,254 - mmtrack - INFO - Epoch [3][150/783]   lr: 1.000e-03, eta: 0:51:32, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0044, loss_cls: 0.0653, acc: 97.4922, loss_bbox: 0.0291, loss: 0.1286, grad_norm: 1.1292
2021-12-15 08:00:48,434 - mmtrack - INFO - Epoch [3][200/783]   lr: 1.000e-03, eta: 0:50:53, time: 0.844, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0066, loss_cls: 0.0564, acc: 97.6172, loss_bbox: 0.0319, loss: 0.1171, grad_norm: 1.3518
2021-12-15 08:01:31,145 - mmtrack - INFO - Epoch [3][250/783]   lr: 1.000e-03, eta: 0:50:15, time: 0.854, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0091, loss_cls: 0.0618, acc: 97.4609, loss_bbox: 0.0378, loss: 0.1381, grad_norm: 1.5622
2021-12-15 08:02:14,347 - mmtrack - INFO - Epoch [3][300/783]   lr: 1.000e-03, eta: 0:49:38, time: 0.864, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0075, loss_cls: 0.0597, acc: 97.2969, loss_bbox: 0.0337, loss: 0.1276, grad_norm: 1.4060
2021-12-15 08:02:58,172 - mmtrack - INFO - Epoch [3][350/783]   lr: 1.000e-03, eta: 0:49:02, time: 0.876, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0342, loss_rpn_bbox: 0.0070, loss_cls: 0.0639, acc: 97.5312, loss_bbox: 0.0337, loss: 0.1388, grad_norm: 1.5456
2021-12-15 08:03:41,040 - mmtrack - INFO - Epoch [3][400/783]   lr: 1.000e-03, eta: 0:48:23, time: 0.857, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0058, loss_cls: 0.0632, acc: 97.6250, loss_bbox: 0.0357, loss: 0.1309, grad_norm: 1.3671
2021-12-15 08:04:23,579 - mmtrack - INFO - Epoch [3][450/783]   lr: 1.000e-03, eta: 0:47:44, time: 0.851, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0087, loss_cls: 0.0608, acc: 97.5000, loss_bbox: 0.0360, loss: 0.1330, grad_norm: 1.4308
2021-12-15 08:05:05,017 - mmtrack - INFO - Epoch [3][500/783]   lr: 1.000e-03, eta: 0:47:03, time: 0.829, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0043, loss_cls: 0.0519, acc: 97.7344, loss_bbox: 0.0340, loss: 0.1096, grad_norm: 1.3007
2021-12-15 08:05:47,805 - mmtrack - INFO - Epoch [3][550/783]   lr: 1.000e-03, eta: 0:46:24, time: 0.856, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0033, loss_cls: 0.0571, acc: 97.7109, loss_bbox: 0.0332, loss: 0.1152, grad_norm: 1.3898
2021-12-15 08:06:30,368 - mmtrack - INFO - Epoch [3][600/783]   lr: 1.000e-03, eta: 0:45:44, time: 0.851, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0049, loss_cls: 0.0634, acc: 97.5078, loss_bbox: 0.0365, loss: 0.1238, grad_norm: 1.5299
2021-12-15 08:07:13,060 - mmtrack - INFO - Epoch [3][650/783]   lr: 1.000e-03, eta: 0:45:05, time: 0.854, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0042, loss_cls: 0.0535, acc: 97.7500, loss_bbox: 0.0282, loss: 0.1071, grad_norm: 1.3469
2021-12-15 08:07:55,473 - mmtrack - INFO - Epoch [3][700/783]   lr: 1.000e-03, eta: 0:44:25, time: 0.848, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0072, loss_cls: 0.0609, acc: 97.3984, loss_bbox: 0.0382, loss: 0.1337, grad_norm: 1.6092
2021-12-15 08:08:38,218 - mmtrack - INFO - Epoch [3][750/783]   lr: 1.000e-03, eta: 0:43:45, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0057, loss_cls: 0.0553, acc: 97.8438, loss_bbox: 0.0328, loss: 0.1234, grad_norm: 1.6894
2021-12-15 08:09:06,489 - mmtrack - INFO - Saving checkpoint at 3 epochs
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
2021-12-15 08:09:54,497 - mmtrack - INFO - Epoch [4][50/783]    lr: 1.000e-03, eta: 0:42:06, time: 0.902, data_time: 0.067, memory: 10834, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0080, loss_cls: 0.0565, acc: 97.7969, loss_bbox: 0.0344, loss: 0.1228, grad_norm: 1.7736
2021-12-15 08:10:37,212 - mmtrack - INFO - Epoch [4][100/783]   lr: 1.000e-03, eta: 0:41:27, time: 0.854, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0038, loss_cls: 0.0641, acc: 97.4453, loss_bbox: 0.0409, loss: 0.1335, grad_norm: 1.5027
2021-12-15 08:11:20,119 - mmtrack - INFO - Epoch [4][150/783]   lr: 1.000e-03, eta: 0:40:48, time: 0.858, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0053, loss_cls: 0.0505, acc: 97.9375, loss_bbox: 0.0323, loss: 0.1037, grad_norm: 1.6031
2021-12-15 08:12:02,739 - mmtrack - INFO - Epoch [4][200/783]   lr: 1.000e-03, eta: 0:40:09, time: 0.852, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0424, loss_rpn_bbox: 0.0064, loss_cls: 0.0704, acc: 97.2031, loss_bbox: 0.0401, loss: 0.1594, grad_norm: 1.8979
2021-12-15 08:12:45,247 - mmtrack - INFO - Epoch [4][250/783]   lr: 1.000e-03, eta: 0:39:29, time: 0.850, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0039, loss_cls: 0.0544, acc: 97.8125, loss_bbox: 0.0308, loss: 0.1065, grad_norm: 1.3586
2021-12-15 08:13:27,776 - mmtrack - INFO - Epoch [4][300/783]   lr: 1.000e-03, eta: 0:38:50, time: 0.851, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0053, loss_cls: 0.0524, acc: 97.8594, loss_bbox: 0.0327, loss: 0.1122, grad_norm: 1.5598
2021-12-15 08:14:10,657 - mmtrack - INFO - Epoch [4][350/783]   lr: 1.000e-03, eta: 0:38:10, time: 0.858, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0046, loss_cls: 0.0509, acc: 98.0000, loss_bbox: 0.0281, loss: 0.1028, grad_norm: 1.4910
2021-12-15 08:14:53,588 - mmtrack - INFO - Epoch [4][400/783]   lr: 1.000e-03, eta: 0:37:31, time: 0.859, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0075, loss_cls: 0.0596, acc: 97.8047, loss_bbox: 0.0349, loss: 0.1195, grad_norm: 1.8067
2021-12-15 08:15:36,287 - mmtrack - INFO - Epoch [4][450/783]   lr: 1.000e-03, eta: 0:36:51, time: 0.854, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0069, loss_cls: 0.0449, acc: 98.1250, loss_bbox: 0.0321, loss: 0.1012, grad_norm: 1.6314
2021-12-15 08:16:18,941 - mmtrack - INFO - Epoch [4][500/783]   lr: 1.000e-03, eta: 0:36:11, time: 0.853, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0083, loss_cls: 0.0501, acc: 97.7812, loss_bbox: 0.0267, loss: 0.1013, grad_norm: 1.7278
2021-12-15 08:17:01,682 - mmtrack - INFO - Epoch [4][550/783]   lr: 1.000e-03, eta: 0:35:31, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0048, loss_cls: 0.0587, acc: 97.7578, loss_bbox: 0.0289, loss: 0.1113, grad_norm: 1.6548
2021-12-15 08:17:44,160 - mmtrack - INFO - Epoch [4][600/783]   lr: 1.000e-03, eta: 0:34:51, time: 0.850, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0064, loss_cls: 0.0448, acc: 98.2109, loss_bbox: 0.0265, loss: 0.0910, grad_norm: 1.5991
2021-12-15 08:18:26,821 - mmtrack - INFO - Epoch [4][650/783]   lr: 1.000e-03, eta: 0:34:11, time: 0.853, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0069, loss_cls: 0.0551, acc: 97.7188, loss_bbox: 0.0303, loss: 0.1152, grad_norm: 2.2151
2021-12-15 08:19:09,555 - mmtrack - INFO - Epoch [4][700/783]   lr: 1.000e-03, eta: 0:33:31, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0036, loss_cls: 0.0522, acc: 97.7734, loss_bbox: 0.0273, loss: 0.0976, grad_norm: 1.6047
2021-12-15 08:19:52,329 - mmtrack - INFO - Epoch [4][750/783]   lr: 1.000e-03, eta: 0:32:50, time: 0.855, data_time: 0.013, memory: 10834, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0055, loss_cls: 0.0465, acc: 98.0547, loss_bbox: 0.0233, loss: 0.0920, grad_norm: 1.6346
2021-12-15 08:20:20,526 - mmtrack - INFO - Saving checkpoint at 4 epochs
[>>                                                ] 995/24789, 3.4 task/s, elapsed: 292s, ETA:  6982s

[>>                                                ] 996/24789, 3.4 task/s, elapsed: 292s, ETA:  6982s
[>>                                                ] 997/24789, 3.4 task/s, elapsed: 293s, ETA:  6982s

[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 24789/24789, 3.5 task/s, elapsed: 7103s, ETA:     0s2021-12-15 10:19:13,262 - mmtrack - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=4.12s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=47.14s).
Accumulating evaluation results...
/root/anaconda3/envs/openmmlab/lib/python3.9/site-packages/pycocotools/cocoeval.py:378: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
DONE (t=32.86s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.017
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.025
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.018
 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 ] = 0.010
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.020
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.023
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.023
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.023
 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 ] = 0.015
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.026
2021-12-15 10:20:45,186 - mmtrack - INFO - Epoch(val) [4][24789]        bbox_mAP: 0.0170, bbox_mAP_50: 0.0250, bbox_mAP_75: 0.0180, bbox_mAP_s: -1.0000, bbox_mAP_m: 0.0100, bbox_mAP_l: 0.0200, bbox_mAP_copypaste: 0.017 0.025 0.018 -1.000 0.010 0.020
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
2021-12-15 10:21:30,741 - mmtrack - INFO - Epoch [5][50/783]    lr: 1.000e-03, eta: 0:31:25, time: 0.911, data_time: 0.069, memory: 10868, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0046, loss_cls: 0.0563, acc: 97.6875, loss_bbox: 0.0332, loss: 0.1098, grad_norm: 1.6906
2021-12-15 10:22:13,173 - mmtrack - INFO - Epoch [5][100/783]   lr: 1.000e-03, eta: 0:30:45, time: 0.849, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0063, loss_cls: 0.0441, acc: 98.2188, loss_bbox: 0.0276, loss: 0.0958, grad_norm: 1.9188
2021-12-15 10:22:56,221 - mmtrack - INFO - Epoch [5][150/783]   lr: 1.000e-03, eta: 0:30:05, time: 0.861, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0061, loss_cls: 0.0534, acc: 97.8281, loss_bbox: 0.0317, loss: 0.1139, grad_norm: 2.0449
2021-12-15 10:23:38,847 - mmtrack - INFO - Epoch [5][200/783]   lr: 1.000e-03, eta: 0:29:25, time: 0.853, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0039, loss_cls: 0.0488, acc: 98.1641, loss_bbox: 0.0261, loss: 0.0975, grad_norm: 1.6397
2021-12-15 10:24:21,980 - mmtrack - INFO - Epoch [5][250/783]   lr: 1.000e-03, eta: 0:28:45, time: 0.863, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0033, loss_cls: 0.0470, acc: 98.0469, loss_bbox: 0.0245, loss: 0.0862, grad_norm: 1.6121
2021-12-15 10:25:05,130 - mmtrack - INFO - Epoch [5][300/783]   lr: 1.000e-03, eta: 0:28:06, time: 0.863, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0071, loss_cls: 0.0463, acc: 98.1250, loss_bbox: 0.0289, loss: 0.0963, grad_norm: 1.9467
2021-12-15 10:25:47,930 - mmtrack - INFO - Epoch [5][350/783]   lr: 1.000e-03, eta: 0:27:25, time: 0.856, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0064, loss_cls: 0.0576, acc: 97.7891, loss_bbox: 0.0299, loss: 0.1153, grad_norm: 1.9621
2021-12-15 10:26:30,898 - mmtrack - INFO - Epoch [5][400/783]   lr: 1.000e-03, eta: 0:26:45, time: 0.859, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0046, loss_cls: 0.0600, acc: 97.6016, loss_bbox: 0.0331, loss: 0.1170, grad_norm: 2.0148
2021-12-15 10:27:13,653 - mmtrack - INFO - Epoch [5][450/783]   lr: 1.000e-03, eta: 0:26:05, time: 0.855, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0021, loss_cls: 0.0490, acc: 98.2656, loss_bbox: 0.0219, loss: 0.0882, grad_norm: 1.7259
2021-12-15 10:27:56,719 - mmtrack - INFO - Epoch [5][500/783]   lr: 1.000e-03, eta: 0:25:25, time: 0.861, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0072, loss_cls: 0.0473, acc: 97.9219, loss_bbox: 0.0285, loss: 0.0977, grad_norm: 1.9972
2021-12-15 10:28:39,762 - mmtrack - INFO - Epoch [5][550/783]   lr: 1.000e-03, eta: 0:24:44, time: 0.861, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0093, loss_cls: 0.0494, acc: 98.0234, loss_bbox: 0.0240, loss: 0.0990, grad_norm: 1.8269
2021-12-15 10:29:22,431 - mmtrack - INFO - Epoch [5][600/783]   lr: 1.000e-03, eta: 0:24:04, time: 0.853, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0078, loss_cls: 0.0416, acc: 98.3047, loss_bbox: 0.0257, loss: 0.0917, grad_norm: 1.8588
2021-12-15 10:30:05,122 - mmtrack - INFO - Epoch [5][650/783]   lr: 1.000e-03, eta: 0:23:23, time: 0.854, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0031, loss_cls: 0.0426, acc: 98.3203, loss_bbox: 0.0250, loss: 0.0840, grad_norm: 1.7312
2021-12-15 10:30:48,007 - mmtrack - INFO - Epoch [5][700/783]   lr: 1.000e-03, eta: 0:22:42, time: 0.858, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0033, loss_cls: 0.0387, acc: 98.3047, loss_bbox: 0.0275, loss: 0.0811, grad_norm: 1.8191
2021-12-15 10:31:30,622 - mmtrack - INFO - Epoch [5][750/783]   lr: 1.000e-03, eta: 0:22:02, time: 0.852, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0045, loss_cls: 0.0456, acc: 98.1797, loss_bbox: 0.0233, loss: 0.0853, grad_norm: 1.7002
2021-12-15 10:31:59,044 - mmtrack - INFO - Saving checkpoint at 5 epochs
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
2021-12-15 10:32:47,033 - mmtrack - INFO - Epoch [6][50/783]    lr: 1.000e-04, eta: 0:20:44, time: 0.906, data_time: 0.069, memory: 10868, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0034, loss_cls: 0.0410, acc: 98.3594, loss_bbox: 0.0214, loss: 0.0807, grad_norm: 1.8961
2021-12-15 10:33:29,982 - mmtrack - INFO - Epoch [6][100/783]   lr: 1.000e-04, eta: 0:20:04, time: 0.859, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0042, loss_cls: 0.0450, acc: 98.2812, loss_bbox: 0.0230, loss: 0.0845, grad_norm: 1.6370
2021-12-15 10:34:12,781 - mmtrack - INFO - Epoch [6][150/783]   lr: 1.000e-04, eta: 0:19:23, time: 0.856, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0027, loss_cls: 0.0375, acc: 98.4453, loss_bbox: 0.0195, loss: 0.0733, grad_norm: 1.5153
2021-12-15 10:34:55,384 - mmtrack - INFO - Epoch [6][200/783]   lr: 1.000e-04, eta: 0:18:43, time: 0.852, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0093, loss_rpn_bbox: 0.0028, loss_cls: 0.0391, acc: 98.3359, loss_bbox: 0.0183, loss: 0.0696, grad_norm: 1.5642
2021-12-15 10:35:38,370 - mmtrack - INFO - Epoch [6][250/783]   lr: 1.000e-04, eta: 0:18:02, time: 0.860, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0034, loss_cls: 0.0385, acc: 98.3359, loss_bbox: 0.0222, loss: 0.0766, grad_norm: 1.7370
2021-12-15 10:36:21,381 - mmtrack - INFO - Epoch [6][300/783]   lr: 1.000e-04, eta: 0:17:22, time: 0.860, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0090, loss_rpn_bbox: 0.0031, loss_cls: 0.0446, acc: 98.1484, loss_bbox: 0.0233, loss: 0.0800, grad_norm: 1.8497
2021-12-15 10:37:04,179 - mmtrack - INFO - Epoch [6][350/783]   lr: 1.000e-04, eta: 0:16:41, time: 0.856, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0085, loss_rpn_bbox: 0.0037, loss_cls: 0.0351, acc: 98.5781, loss_bbox: 0.0186, loss: 0.0659, grad_norm: 1.6877
2021-12-15 10:37:47,006 - mmtrack - INFO - Epoch [6][400/783]   lr: 1.000e-04, eta: 0:16:00, time: 0.857, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0088, loss_rpn_bbox: 0.0027, loss_cls: 0.0376, acc: 98.3594, loss_bbox: 0.0200, loss: 0.0691, grad_norm: 1.7364
2021-12-15 10:38:29,817 - mmtrack - INFO - Epoch [6][450/783]   lr: 1.000e-04, eta: 0:15:19, time: 0.856, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0032, loss_cls: 0.0385, acc: 98.4219, loss_bbox: 0.0181, loss: 0.0736, grad_norm: 1.5982
2021-12-15 10:39:12,682 - mmtrack - INFO - Epoch [6][500/783]   lr: 1.000e-04, eta: 0:14:39, time: 0.857, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0038, loss_cls: 0.0397, acc: 98.4844, loss_bbox: 0.0230, loss: 0.0783, grad_norm: 1.7266
2021-12-15 10:39:55,418 - mmtrack - INFO - Epoch [6][550/783]   lr: 1.000e-04, eta: 0:13:58, time: 0.855, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0041, loss_cls: 0.0454, acc: 98.0625, loss_bbox: 0.0233, loss: 0.0901, grad_norm: 1.9113
2021-12-15 10:40:38,197 - mmtrack - INFO - Epoch [6][600/783]   lr: 1.000e-04, eta: 0:13:17, time: 0.856, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0042, loss_cls: 0.0485, acc: 98.3984, loss_bbox: 0.0237, loss: 0.0880, grad_norm: 1.8074
2021-12-15 10:41:20,745 - mmtrack - INFO - Epoch [6][650/783]   lr: 1.000e-04, eta: 0:12:36, time: 0.851, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0035, loss_cls: 0.0552, acc: 97.9062, loss_bbox: 0.0310, loss: 0.1069, grad_norm: 2.0954
2021-12-15 10:42:03,730 - mmtrack - INFO - Epoch [6][700/783]   lr: 1.000e-04, eta: 0:11:55, time: 0.860, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0040, loss_cls: 0.0389, acc: 98.5078, loss_bbox: 0.0204, loss: 0.0800, grad_norm: 1.7951
2021-12-15 10:42:46,782 - mmtrack - INFO - Epoch [6][750/783]   lr: 1.000e-04, eta: 0:11:14, time: 0.861, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0092, loss_rpn_bbox: 0.0020, loss_cls: 0.0365, acc: 98.5000, loss_bbox: 0.0212, loss: 0.0689, grad_norm: 1.6554
2021-12-15 10:43:15,304 - mmtrack - INFO - Saving checkpoint at 6 epochs
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
/share_data/YuhaoSun/mmtracking-master/mmtracking-master/mmtrack/datasets/coco_video_dataset.py:359: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  ann['instance_ids'] = np.array(gt_instance_ids).astype(np.int)
2021-12-15 10:44:03,754 - mmtrack - INFO - Epoch [7][50/783]    lr: 1.000e-04, eta: 0:10:02, time: 0.918, data_time: 0.070, memory: 10868, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0037, loss_cls: 0.0418, acc: 98.2422, loss_bbox: 0.0236, loss: 0.0815, grad_norm: 1.8862
2021-12-15 10:44:46,334 - mmtrack - INFO - Epoch [7][100/783]   lr: 1.000e-04, eta: 0:09:21, time: 0.852, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0018, loss_cls: 0.0349, acc: 98.4297, loss_bbox: 0.0189, loss: 0.0638, grad_norm: 1.5520
2021-12-15 10:45:29,081 - mmtrack - INFO - Epoch [7][150/783]   lr: 1.000e-04, eta: 0:08:40, time: 0.855, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0100, loss_rpn_bbox: 0.0036, loss_cls: 0.0417, acc: 98.3359, loss_bbox: 0.0254, loss: 0.0806, grad_norm: 1.8978
2021-12-15 10:46:12,001 - mmtrack - INFO - Epoch [7][200/783]   lr: 1.000e-04, eta: 0:07:59, time: 0.858, data_time: 0.013, memory: 10868, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0031, loss_cls: 0.0402, acc: 98.3828, loss_bbox: 0.0219, loss: 0.0731, grad_norm: 1.8422
2021-12-15 10:46:54,684 - mmtrack - INFO - Epoch [7][250/783]   lr: 1.000e-04, eta: 0:07:18, time: 0.854, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0033, loss_cls: 0.0488, acc: 98.2031, loss_bbox: 0.0215, loss: 0.0912, grad_norm: 2.1403
2021-12-15 10:47:37,306 - mmtrack - INFO - Epoch [7][300/783]   lr: 1.000e-04, eta: 0:06:37, time: 0.852, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0018, loss_cls: 0.0330, acc: 98.6719, loss_bbox: 0.0168, loss: 0.0688, grad_norm: 1.6002
2021-12-15 10:48:19,800 - mmtrack - INFO - Epoch [7][350/783]   lr: 1.000e-04, eta: 0:05:56, time: 0.850, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0103, loss_rpn_bbox: 0.0040, loss_cls: 0.0434, acc: 98.1250, loss_bbox: 0.0226, loss: 0.0803, grad_norm: 1.9011
2021-12-15 10:49:02,646 - mmtrack - INFO - Epoch [7][400/783]   lr: 1.000e-04, eta: 0:05:15, time: 0.857, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0031, loss_cls: 0.0388, acc: 98.4453, loss_bbox: 0.0183, loss: 0.0714, grad_norm: 1.5801
2021-12-15 10:49:45,282 - mmtrack - INFO - Epoch [7][450/783]   lr: 1.000e-04, eta: 0:04:34, time: 0.853, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0095, loss_rpn_bbox: 0.0027, loss_cls: 0.0379, acc: 98.4609, loss_bbox: 0.0169, loss: 0.0670, grad_norm: 1.7053
2021-12-15 10:50:28,186 - mmtrack - INFO - Epoch [7][500/783]   lr: 1.000e-04, eta: 0:03:53, time: 0.858, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0034, loss_cls: 0.0454, acc: 98.2500, loss_bbox: 0.0246, loss: 0.0908, grad_norm: 2.0075
2021-12-15 10:51:10,871 - mmtrack - INFO - Epoch [7][550/783]   lr: 1.000e-04, eta: 0:03:12, time: 0.854, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0094, loss_rpn_bbox: 0.0023, loss_cls: 0.0417, acc: 98.2188, loss_bbox: 0.0214, loss: 0.0748, grad_norm: 1.7527
2021-12-15 10:51:53,673 - mmtrack - INFO - Epoch [7][600/783]   lr: 1.000e-04, eta: 0:02:30, time: 0.856, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0055, loss_cls: 0.0439, acc: 98.0859, loss_bbox: 0.0238, loss: 0.0862, grad_norm: 2.0178
2021-12-15 10:52:36,401 - mmtrack - INFO - Epoch [7][650/783]   lr: 1.000e-04, eta: 0:01:49, time: 0.855, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0024, loss_cls: 0.0379, acc: 98.4609, loss_bbox: 0.0177, loss: 0.0698, grad_norm: 1.8297
2021-12-15 10:53:19,246 - mmtrack - INFO - Epoch [7][700/783]   lr: 1.000e-04, eta: 0:01:08, time: 0.857, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0032, loss_cls: 0.0441, acc: 98.4609, loss_bbox: 0.0203, loss: 0.0791, grad_norm: 1.8134
2021-12-15 10:54:01,949 - mmtrack - INFO - Epoch [7][750/783]   lr: 1.000e-04, eta: 0:00:27, time: 0.854, data_time: 0.014, memory: 10868, loss_rpn_cls: 0.0105, loss_rpn_bbox: 0.0028, loss_cls: 0.0387, acc: 98.4453, loss_bbox: 0.0183, loss: 0.0703, grad_norm: 1.8183
2021-12-15 10:54:30,340 - mmtrack - INFO - Saving checkpoint at 7 epochs
meikorol commented 1 year ago

HI，have you solved it？ I have no idea how to make my own dataset and train it，could you plz tell something？