您好,非常好的工作！这里有几个问题想向您请教下。

1. 请问论文中表格4-6的结果是在验证集上测试的呢？还是test结果？代码上的验证过程是基于DOTA1_0_trainval1024.json 和HRSC_L1_train.json的

2. 在做完执行完prepare_dota.py之后,我发现不同类别之间instance数量的差异还是很大的(这里Gaps设置的是200)？不知道这个问题您有没有遇到过？ prepare_dota.py 的setting 如下:

Sub patch size: 1024
Gaps: 200
Data type: dota10
Processor number: 16
Multi scale: False
------------------------------
padding: True

详细的类别实例个数的统计如下： [(1, 19647), (2, 1136), (3, 4317), (4, 832), (5, 59578), (6, 43930), (7, 77605), (8, 6093), (9, 1258), (10, 13941), (11, 925), (12, 1002), (13, 16286), (14, 3925), (15, 1260)]

hello, thanks for your awesome work : ) I am trying to convert the HRSC dataset to coco format so that I can train with it. Is there any tool available in the repo to do that? I have found this tool:

python tools/dataset_converters/pascal_voc.py ${DEVKIT_PATH} [-h] [-o ${OUT_DIR}]

However, when I run it, it says:

Traceback (most recent call last):
  File "tools/dataset_converters/pascal_voc.py", line 236, in <module>
    main()
  File "tools/dataset_converters/pascal_voc.py", line 210, in main
    raise IOError(f'The devkit path {devkit_path} contains neither '
OSError: The devkit path data/HRSC2016/FullDataSet/ contains neither "VOC2007" nor "VOC2012" subfolder

Do you have any tip for that?

Hi there,

I train by leaving the defaults. Specifically, I used:

./tools/dist_train.sh configs/fcosrbox/fcosr_rx50_32x4d_fpn_3x_dota10_single.py 0,1,2,3 --seed 129 --deterministic

Then I measure mAP by executing:

python tools/test.py configs/fcosrbox/fcosr_rx50_32x4d_fpn_3x_dota10_single.py work_dirs/DOTA10/FCOSR-M/FCOSR_rx50_32x4d_fpn_3x_dota10_single/latest.pth --eval mAP

My result is:

npos num: 0
{'iou_50': {'mAp': 0.0, 'detail': {'plane': 0.0, 'baseball-diamond': 0.0, 'bridge': 0.0, 'ground-track-field': 0.0, 'small-vehicle': 0.0, 'large-vehicle': 0.0, 'ship': 0.0, 'tennis-court': 0.0, 'basketball-court': 0.0, 'storage-tank': 0.0, 'soccer-ball-field': 0.0, 'roundabout': 0.0, 'harbor': 0.0, 'swimming-pool': 0.0, 'helicopter': 0.0}}}

What am I missing? I have tried with 3 seeds: 98 , 8 and 129. With the same result

作者你好，感谢你的工作，我对无锚的遥感检测也比较感兴趣，想尝试着复现工作。但是遇到了这个问题。我创建了虚拟环境 安装了pytorch、torchvision.编译了 FCOSR和DOTA_devkit. 结果我python train.py config时 出现了onnx这个属性没有？我是遗漏了什么工作吗？我不能直接在pytorch上实验吗？后面的TensorRT我没用到 是不是这个原因呢？

Hi, nice work!

In paper, there is '*'indicates multi-scale training and testing,'**' means rotation test mode during multi-scale testing,what's the difference between '*' and '**'? and how to set rotation test mode during multi-scale testing?

Thanks！

When I use DOTA_devkit, error occured: Traceback (most recent call last): File "DOTA_devkit/prepare_dota.py", line 195, in print_arg(args) File "DOTA_devkit/prepare_dota.py", line 34, in print_arg if scales.scales: NameError: name 'scales' is not defined

replace 'scales' with 'args'

Hello author, thank you for your work. I want to learn the Multi-level sampling strategy(MLS) part in detail, but I can't find it in the code. Please tell me where MLS is in the code, thanks!

作者大佬您好！ 拜读了您的论文后，备受启发！这两天在阅读您的FCOSRboxHead代码。但是，为何line720：gt_bboxes = self.rotate2rect(gt_rboxes)；line729： ngds_score = self.get_ngds_score(xs, ys, gt_rboxes, mode='shrink', version='v2')；line730：gds_score = self.get_gds_score(xs, ys, gt_rboxes, mode='shrink', refined=True)；line733：inside_gt_rbox_mask, gt_rboxes_idx = self.get_rotate_inside_mask_with_gds(xs, ys, gt_rboxes, 0.23, ngds_score, True)等这些函数里面全是空？另外：还有一个问题，在训练的时候，损失都很正常，但是训练完每一个epoch进行验证的时候，各项指标均为0？我是按照作者get_start里对dota数据集进行划分的。 盼回复！祝生活愉快！

2022-06-07 19:45:48,620 - mmdet - INFO - initialize MobileNetV2_N with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://mmdet/mobilenet_v2'} 2022-06-07 19:45:48,620 - mmcv - INFO - load model from: open-mmlab://mmdet/mobilenet_v2 2022-06-07 19:45:48,621 - mmcv - INFO - Use load_from_openmmlab loader 2022-06-07 19:45:48,637 - mmcv - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: conv2.conv.weight, conv2.bn.weight, conv2.bn.bias, conv2.bn.running_mean, conv2.bn.running_var, conv2.bn.num_batches_tracked

2022-06-07 19:45:48,642 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2022-06-07 19:45:48,645 - mmdet - INFO - initialize FCOSRboxHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'fcos_cls', 'std': 0.01, 'bias_prob': 0.01}} loading annotations into memory... Done (t=0.98s) creating index... index created!

你好, I would like to know what is the meaning of this line:

regress_weight=dict(type='iou')),

It is in the config file, here I find it confusing because the regression loss is already set as ProbiouLoss with mode L1 and loss_weight=1 as per:

regress=[dict(type='ProbiouLoss', mode='l1', loss_weight=1.0)],

So what is the argument regress_weight for?

谢谢