What should I do if the detectron2 removes the instances though the dataset is already in the COCO format?

[cnr0724]

Instructions To Reproduce the 🐛 Bug:

1. Full runnable code or full changes you made: Nothing. I just tried to run code with custom datasets

2. What exact command you run:

3. Full logs or other relevant observations:

   
   [04/14 08:28:29 d2.data.datasets.coco]: Loading /root/visdrone/train/instances_train.json takes 2.98 seconds.
   [04/14 08:28:29 d2.data.datasets.coco]: Loaded 9344 images in COCO format from /root/visdrone/train/instances_train.json
   WARNING [04/14 08:28:30 d2.data.datasets.coco]: Filtered out 506985 instances without valid segmentation. There might be issues in your dataset generation process. A valid polygon should be a list[float] with even length >= 6.
   WARNING [04/14 08:28:30 d2.config.compat]: Config '/root/CenterNet2/projects/CenterNet2/configs/CenterNet2_DLA-BiFPN-P5_640_24x_ST.yaml' has no VERSION. Assuming it to be compatible with latest v2.
   Loading pretrained DLA!
   GeneralizedRCNN(
   (backbone): BiFPN(
   (bottom_up): BackboneWithTopLevels(
     (backbone): DLA(
       (base_layer): Sequential(
         (0): Conv2d(3, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), bias=False)
         (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (2): ReLU(inplace=True)
       )
       (level0): Sequential(
         (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
         (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (2): ReLU(inplace=True)
       )
       (level1): Sequential(
         (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
         (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (2): ReLU(inplace=True)
       )
       (level2): Tree(
         (tree1): BasicBlock(
           (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
           (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           (relu): ReLU(inplace=True)
           (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
           (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (tree2): BasicBlock(
           (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
           (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           (relu): ReLU(inplace=True)
           (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
           (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (root): Root(
           (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
           (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           (relu): ReLU(inplace=True)
         )
         (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
         (project): Sequential(
           (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
           (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
       )
       (level3): Tree(
         (tree1): Tree(
           (tree1): BasicBlock(
             (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (tree2): BasicBlock(
             (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (root): Root(
             (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
             (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
           )
           (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
           (project): Sequential(
             (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
             (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
         )
         (tree2): Tree(
           (tree1): BasicBlock(
             (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (tree2): BasicBlock(
             (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (root): Root(
             (conv): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
             (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
           )
         )
         (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
         (project): Sequential(
           (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
           (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
       )
       (level4): Tree(
         (tree1): Tree(
           (tree1): BasicBlock(
             (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (tree2): BasicBlock(
             (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (root): Root(
             (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
             (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
           )
           (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
           (project): Sequential(
             (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
             (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
         )
         (tree2): Tree(
           (tree1): BasicBlock(
             (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (tree2): BasicBlock(
             (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
             (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
             (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           )
           (root): Root(
             (conv): Conv2d(896, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
             (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
             (relu): ReLU(inplace=True)
           )
         )
         (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
         (project): Sequential(
           (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
           (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
       )
       (level5): Tree(
         (tree1): BasicBlock(
           (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
           (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           (relu): ReLU(inplace=True)
           (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
           (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (tree2): BasicBlock(
           (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
           (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           (relu): ReLU(inplace=True)
           (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
           (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (root): Root(
           (conv): Conv2d(1280, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
           (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
           (relu): ReLU(inplace=True)
         )
         (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
         (project): Sequential(
           (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
           (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
       )
     )
     (dla6): FeatureMapResampler(
       (reduction): Conv2d(
         512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
     )
     (dla7): FeatureMapResampler()
   )
   (repeated_bifpn): ModuleList(
     (0): SingleBiFPN(
       (outputs_f3_3_4): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (lateral_2_f2): Conv2d(
         512, 160, kernel_size=(1, 1), stride=(1, 1)
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f2_2_5): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (lateral_1_f1): Conv2d(
         256, 160, kernel_size=(1, 1), stride=(1, 1)
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f1_1_6): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (lateral_0_f0): Conv2d(
         128, 160, kernel_size=(1, 1), stride=(1, 1)
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f0_0_7): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f1_1_7_8): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f2_2_6_9): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f3_3_5_10): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f4_4_11): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
     )
     (1): SingleBiFPN(
       (outputs_f3_3_4): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f2_2_5): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f1_1_6): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f0_0_7): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f1_1_7_8): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f2_2_6_9): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f3_3_5_10): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f4_4_11): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
     )
     (2): SingleBiFPN(
       (outputs_f3_3_4): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f2_2_5): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f1_1_6): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f0_0_7): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f1_1_7_8): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f2_2_6_9): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f3_3_5_10): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
       (outputs_f4_4_11): Conv2d(
         160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
         (norm): GroupNorm(32, 160, eps=1e-05, affine=True)
       )
     )
   )
   )
   (proposal_generator): CenterNet(
   (iou_loss): IOULoss()
   (centernet_head): CenterNetHead(
     (cls_tower): Sequential()
     (bbox_tower): Sequential(
       (0): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (1): GroupNorm(32, 160, eps=1e-05, affine=True)
       (2): ReLU()
       (3): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (4): GroupNorm(32, 160, eps=1e-05, affine=True)
       (5): ReLU()
       (6): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (7): GroupNorm(32, 160, eps=1e-05, affine=True)
       (8): ReLU()
       (9): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (10): GroupNorm(32, 160, eps=1e-05, affine=True)
       (11): ReLU()
     )
     (share_tower): Sequential()
     (bbox_pred): Conv2d(160, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
     (scales): ModuleList(
       (0): Scale()
       (1): Scale()
       (2): Scale()
       (3): Scale()
       (4): Scale()
     )
     (agn_hm): Conv2d(160, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
   )
   )
   (roi_heads): CustomCascadeROIHeads(
   (box_pooler): ROIPooler(
     (level_poolers): ModuleList(
       (0): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
       (1): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
       (2): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
       (3): ROIAlign(output_size=(7, 7), spatial_scale=0.015625, sampling_ratio=0, aligned=True)
       (4): ROIAlign(output_size=(7, 7), spatial_scale=0.0078125, sampling_ratio=0, aligned=True)
     )
   )
   (box_head): ModuleList(
     (0): FastRCNNConvFCHead(
       (flatten): Flatten(start_dim=1, end_dim=-1)
       (fc1): Linear(in_features=7840, out_features=1024, bias=True)
       (fc_relu1): ReLU()
       (fc2): Linear(in_features=1024, out_features=1024, bias=True)
       (fc_relu2): ReLU()
     )
     (1): FastRCNNConvFCHead(
       (flatten): Flatten(start_dim=1, end_dim=-1)
       (fc1): Linear(in_features=7840, out_features=1024, bias=True)
       (fc_relu1): ReLU()
       (fc2): Linear(in_features=1024, out_features=1024, bias=True)
       (fc_relu2): ReLU()
     )
     (2): FastRCNNConvFCHead(
       (flatten): Flatten(start_dim=1, end_dim=-1)
       (fc1): Linear(in_features=7840, out_features=1024, bias=True)
       (fc_relu1): ReLU()
       (fc2): Linear(in_features=1024, out_features=1024, bias=True)
       (fc_relu2): ReLU()
     )
   )
   (box_predictor): ModuleList(
     (0): CustomFastRCNNOutputLayers(
       (cls_score): Linear(in_features=1024, out_features=11, bias=True)
       (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)
     )
     (1): CustomFastRCNNOutputLayers(
       (cls_score): Linear(in_features=1024, out_features=11, bias=True)
       (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)
     )
     (2): CustomFastRCNNOutputLayers(
       (cls_score): Linear(in_features=1024, out_features=11, bias=True)
       (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)
     )
   )
   )
   )
   [04/14 08:28:35 d2.data.datasets.coco]: Loading /root/visdrone/train/instances_train.json takes 2.95 seconds.
   [04/14 08:28:35 d2.data.datasets.coco]: Loaded 9344 images in COCO format from /root/visdrone/train/instances_train.json
   WARNING [04/14 08:28:36 d2.data.datasets.coco]: Filtered out 506985 instances without valid segmentation. There might be issues in your dataset generation process. A valid polygon should be a list[float] with even length >= 6.
   [04/14 08:28:36 d2.data.build]: Removed 9344 images with no usable annotations. 0 images left.
   [04/14 08:28:36 d2.data.build]: Distribution of instances among all 11 categories:
   |  category  | #instances   |   category    | #instances   |  category  | #instances   |
   |:----------:|:-------------|:-------------:|:-------------|:----------:|:-------------|
   | pedestrian | 0            |    people     | 0            |  bicycle   | 0            |
   |    car     | 0            |      van      | 0            |   truck    | 0            |
   |  tricycle  | 0            | awning-tric.. | 0            |    bus     | 0            |
   |   motor    | 0            |    others     | 0            |            |              |
   |   total    | 0            |               |              |            |              |
   Traceback (most recent call last):
   File "train.py", line 49, in <module>
   trainer = DefaultTrainer(cfg)
   File "/root/detectron2/detectron2/engine/defaults.py", line 312, in __init__
   data_loader = self.build_train_loader(cfg)
   File "/root/detectron2/detectron2/engine/defaults.py", line 485, in build_train_loader
   return build_detection_train_loader(cfg)
   File "/root/detectron2/detectron2/config/config.py", line 192, in wrapped
   explicit_args = _get_args_from_config(from_config, *args, **kwargs)
   File "/root/detectron2/detectron2/config/config.py", line 229, in _get_args_from_config
   ret = from_config_func(*args, **kwargs)
   File "/root/detectron2/detectron2/data/build.py", line 308, in _train_loader_from_config
   dataset = get_detection_dataset_dicts(
   File "/root/detectron2/detectron2/data/build.py", line 255, in get_detection_dataset_dicts
   assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
   AssertionError: No valid data found in dronedataset.

4. please simplify the steps as much as possible so they do not require additional resources to
   run, such as a private dataset.

## Expected behavior:

If there are no obvious error in "full logs" provided above,
please tell us the expected behavior.

## Environment:

Provide your environment information using the following command:

---

sys.platform linux Python 3.8.3 (default, May 19 2020, 18:47:26) [GCC 7.3.0] numpy 1.19.2 detectron2 0.4 @/root/detectron2/detectron2 Compiler GCC 7.5 CUDA compiler CUDA 11.0 detectron2 arch flags 8.0 DETECTRON2_ENV_MODULE PyTorch 1.7.0 @/opt/conda/lib/python3.8/site-packages/torch PyTorch debug build True GPU available True GPU 0 GeForce RTX 3090 (arch=8.6) CUDA_HOME /usr/local/cuda Pillow 8.0.0 torchvision 0.8.0 @/opt/conda/lib/python3.8/site-packages/torchvision torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0 fvcore 0.1.5.post20210402 iopath 0.1.7 cv2 4.5.1

---

PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 11.0
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_37,code=compute_37
• CuDNN 8.0.3
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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,

If your issue looks like an installation issue / environment issue,
please first try to solve it yourself with the instructions in
https://detectron2.readthedocs.io/tutorials/install.html#common-installation-issues

[ppwwyyxx]

As the log says:

There might be issues in your dataset generation process. A valid polygon should be a list[float] with even length >= 6.

[cnr0724]

It already is. One of the polygon is like {"id": 1, "image_id": 0, "category_id": 4, "segmentation": [[948.0, 592.0], [1010.0, 592.0], [948.0, 684.0], [1010.0, 684.0]], "area": 5704.0, "bbox": [948.0, 592.0, 62.0, 92.0], "iscrowd": 0}.

[ppwwyyxx]

"segmentation" in the above comment is not in the format documented at https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html.

[[948.0, 592.0, 1010.0, 592.0, 948.0, 684.0, 1010.0, 684.0]] looks correct.

[cnr0724]

Thank you!