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custom model doesn't work

See original GitHub issue

Prerequisite

🐞 Describe the bug

I added ‘mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco_mydata.py’

_base_ = [
    '../_base_/models/mask_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

model = dict(
    roi_head=dict(
        bbox_head=dict(num_classes=2),
        mask_head=dict(num_classes=2)))

dataset_type = 'CocoDataset'
data_root = 'data/coco/'

data = dict(
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations.json',
        img_prefix=data_root),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations.json',
        img_prefix=data_root),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations.json',
        img_prefix=data_root))
evaluation = dict(metric=['bbox', 'segm'])

# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)

I modified ‘mmdet/core/evaluation/class_names.py’

def coco_classes():
    return [
        '_background_', 'flash_light'
    ]

I modified ‘mmdet/datasets/coco.py’

CLASSES = ('_background_', 'flash_light')

PALETTE = [(0, 0, 0), (0, 0, 128)]

I get a log file

2022-10-14 15:32:36,204 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2022-10-14 15:32:36,204 - mmdet - INFO - Checkpoints will be saved to /home/unstruct/combination/mmdetection/work_dirs/mask_rcnn_r50_fpn_1x_coco_mydata by HardDiskBackend.
2022-10-14 15:32:39,200 - mmdet - INFO - Saving checkpoint at 1 epochs
2022-10-14 15:32:40,275 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:32:40,308 - mmdet - INFO - 
 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 ] = 0.000
 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 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 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 ] = 0.000

2022-10-14 15:32:40,308 - mmdet - INFO - Evaluating segm...
2022-10-14 15:32:40,356 - mmdet - INFO - 
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.001
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 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 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.008
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000

2022-10-14 15:32:40,356 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:32:40,356 - mmdet - INFO - Epoch(val) [1][11]	bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000, segm_mAP: 0.0000, segm_mAP_50: 0.0010, segm_mAP_75: 0.0000, segm_mAP_s: 0.0000, segm_mAP_m: 0.0000, segm_mAP_l: 0.0000, segm_mAP_copypaste: 0.000 0.001 0.000 0.000 0.000 0.000
2022-10-14 15:32:43,315 - mmdet - INFO - Saving checkpoint at 2 epochs
2022-10-14 15:32:44,381 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:32:44,421 - mmdet - INFO - 
 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 ] = 0.000
 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 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.008
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000

2022-10-14 15:32:44,421 - mmdet - INFO - Evaluating segm...
2022-10-14 15:32:44,484 - mmdet - INFO - 
 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 ] = 0.000
 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 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 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 ] = 0.000

2022-10-14 15:32:44,485 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:32:44,485 - mmdet - INFO - Epoch(val) [2][11]	bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000, segm_mAP: 0.0000, segm_mAP_50: 0.0000, segm_mAP_75: 0.0000, segm_mAP_s: 0.0000, segm_mAP_m: 0.0000, segm_mAP_l: 0.0000, segm_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000
2022-10-14 15:32:47,480 - mmdet - INFO - Saving checkpoint at 3 epochs
2022-10-14 15:32:48,561 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:32:48,603 - mmdet - INFO - 
 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 ] = 0.000
 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 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.008
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000

2022-10-14 15:32:48,603 - mmdet - INFO - Evaluating segm...
2022-10-14 15:32:48,669 - mmdet - INFO - 
 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 ] = 0.000
 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 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 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 ] = 0.000

2022-10-14 15:32:48,670 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:32:48,670 - mmdet - INFO - Epoch(val) [3][11]	bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000, segm_mAP: 0.0000, segm_mAP_50: 0.0000, segm_mAP_75: 0.0000, segm_mAP_s: 0.0000, segm_mAP_m: 0.0000, segm_mAP_l: 0.0000, segm_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000
2022-10-14 15:32:51,667 - mmdet - INFO - Saving checkpoint at 4 epochs
2022-10-14 15:32:52,494 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:32:52,501 - mmdet - INFO - 
 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 ] = 0.000
 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 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 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 ] = 0.000

2022-10-14 15:32:52,501 - mmdet - INFO - Evaluating segm...
2022-10-14 15:32:52,506 - mmdet - INFO - 
 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 ] = 0.000
 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 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
 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 ] = 0.000

2022-10-14 15:32:52,506 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:32:52,506 - mmdet - INFO - Epoch(val) [4][11]	bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000, segm_mAP: 0.0000, segm_mAP_50: 0.0000, segm_mAP_75: 0.0000, segm_mAP_s: 0.0000, segm_mAP_m: 0.0000, segm_mAP_l: 0.0000, segm_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000
2022-10-14 15:32:55,488 - mmdet - INFO - Saving checkpoint at 5 epochs
2022-10-14 15:32:56,297 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:32:56,297 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:32:56,297 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:32:56,297 - mmdet - INFO - Epoch(val) [5][11]	
2022-10-14 15:32:59,288 - mmdet - INFO - Saving checkpoint at 6 epochs
2022-10-14 15:33:00,108 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:33:00,108 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:33:00,108 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:33:00,108 - mmdet - INFO - Epoch(val) [6][11]	
2022-10-14 15:33:03,106 - mmdet - INFO - Saving checkpoint at 7 epochs
2022-10-14 15:33:03,924 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:33:03,924 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:33:03,925 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:33:03,925 - mmdet - INFO - Epoch(val) [7][11]	
2022-10-14 15:33:06,938 - mmdet - INFO - Saving checkpoint at 8 epochs
2022-10-14 15:33:07,776 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:33:07,777 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:33:07,777 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:33:07,777 - mmdet - INFO - Epoch(val) [8][11]	
2022-10-14 15:33:10,800 - mmdet - INFO - Saving checkpoint at 9 epochs
2022-10-14 15:33:11,632 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:33:11,632 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:33:11,633 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:33:11,633 - mmdet - INFO - Epoch(val) [9][11]	
2022-10-14 15:33:14,696 - mmdet - INFO - Saving checkpoint at 10 epochs
2022-10-14 15:33:15,530 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:33:15,530 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:33:15,531 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:33:15,531 - mmdet - INFO - Epoch(val) [10][11]	
2022-10-14 15:33:18,518 - mmdet - INFO - Saving checkpoint at 11 epochs
2022-10-14 15:33:19,347 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:33:19,348 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:33:19,348 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:33:19,348 - mmdet - INFO - Epoch(val) [11][11]	
2022-10-14 15:33:22,329 - mmdet - INFO - Saving checkpoint at 12 epochs
2022-10-14 15:33:23,155 - mmdet - INFO - Evaluating bbox...
2022-10-14 15:33:23,155 - mmdet - ERROR - The testing results of the whole dataset is empty.
2022-10-14 15:33:23,155 - mmdet - INFO - Exp name: mask_rcnn_r50_fpn_1x_coco_mydata.py
2022-10-14 15:33:23,155 - mmdet - INFO - Epoch(val) [12][11]

when I run demo/image_demo.py using custom checkpoint and custom config file, I got just original image(nothing changed)

Environment

sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True GPU 0: NVIDIA GeForce RTX 3080 Ti CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.1, V11.1.105 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.10.1+cu111 PyTorch compiling details: PyTorch built with:

  • GCC 7.3
  • C++ Version: 201402
  • Intel® Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel® 64 architecture applications
  • Intel® MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  • OpenMP 201511 (a.k.a. OpenMP 4.5)
  • LAPACK is enabled (usually provided by MKL)
  • NNPACK is enabled
  • CPU capability usage: AVX2
  • CUDA Runtime 11.1
  • NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  • CuDNN 8.0.5
  • Magma 2.5.2
  • Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.2+cu111 OpenCV: 4.6.0 MMCV: 1.6.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.1 MMDetection: 2.25.0+ca11860

Additional information

No response

Issue Analytics

  • State:closed
  • Created a year ago
  • Comments:8

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1reaction
wwcc1107commented, Nov 9, 2022

try to change lr and warmup to fix this problem

thank you very much,I check my datasets,it is a problem in data

0reactions
wwcc1107commented, Nov 29, 2022

it does not conform to the format specification of the standard COCO dataset

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