FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

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Deep Learning FCOSR
Overview

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection
arXiv preprint (arXiv:2111.10780).

This implement is modified from mmdetection. We also refer to the codes of ReDet, PIoU, and ProbIoU.

In the process of implementation, we find that only Python code processing will produce huge memory overhead on Nvidia devices. Therefore, we directly write the label assignment module proposed in this paper in the form of CUDA extension of Pytorch. The program could not work effectively when we migrate it to cuda 11 (only support cuda10). By applying CUDA expansion, the memory utilization is improved and a lot of unnecessary calculations are reduced. We also try to train FCOSR-M on 2080ti (4 images per device), which can basically fill memory of graphics card.

Install

Please refer to install.md for installation and dataset preparation.

Getting Started

Please see get_started.md for the basic usage.

Model Zoo

benchmark

The password of baiduPan is ABCD

FCOSR serise DOTA 1.0 result.FPS(2080ti) Detail

Model backbone MS Sched. Param. Input GFLOPs FPS mAP download
FCOSR-S Mobilenet v2 - 3x 7.32M 1024×1024 101.42 23.7 74.05 model/cfg
FCOSR-S Mobilenet v2 3x 7.32M 1024×1024 101.42 23.7 76.11 model/cfg
FCOSR-M ResNext50-32x4 - 3x 31.4M 1024×1024 210.01 14.6 77.15 model/cfg
FCOSR-M ResNext50-32x4 3x 31.4M 1024×1024 210.01 14.6 79.25 model/cfg
FCOSR-L ResNext101-64x4 - 3x 89.64M 1024×1024 445.75 7.9 77.39 model/cfg
FCOSR-L ResNext101-64x4 3x 89.64M 1024×1024 445.75 7.9 78.80 model/cfg

FCOSR serise DOTA 1.5 result. FPS(2080ti) Detail

Model backbone MS Sched. Param. Input GFLOPs FPS mAP download
FCOSR-S Mobilenet v2 - 3x 7.32M 1024×1024 101.42 23.7 66.37 model/cfg
FCOSR-S Mobilenet v2 3x 7.32M 1024×1024 101.42 23.7 73.14 model/cfg
FCOSR-M ResNext50-32x4 - 3x 31.4M 1024×1024 210.01 14.6 68.74 model/cfg
FCOSR-M ResNext50-32x4 3x 31.4M 1024×1024 210.01 14.6 73.79 model/cfg
FCOSR-L ResNext101-64x4 - 3x 89.64M 1024×1024 445.75 7.9 69.96 model/cfg
FCOSR-L ResNext101-64x4 3x 89.64M 1024×1024 445.75 7.9 75.41 model/cfg

FCOSR serise HRSC2016 result. FPS(2080ti)

Model backbone Rot. Sched. Param. Input GFLOPs FPS AP50(07) AP75(07) AP50(12) AP75(12) download
FCOSR-S Mobilenet v2 40k iters 7.29M 800×800 61.57 35.3 90.08 76.75 92.67 75.73 model/cfg
FCOSR-M ResNext50-32x4 40k iters 31.37M 800×800 127.87 26.9 90.15 78.58 94.84 81.38 model/cfg
FCOSR-L ResNext101-64x4 40k iters 89.61M 800×800 271.75 15.1 90.14 77.98 95.74 80.94 model/cfg

Lightweight FCOSR test result on Jetson Xavier NX (DOTA 1.0 single-scale). Detail

Model backbone Head channels Sched. Param Size Input GFLOPs FPS mAP onnx TensorRT
FCOSR-lite Mobilenet v2 256 3x 6.9M 51.63MB 1024×1024 101.25 7.64 74.30 Wait rtr
FCOSR-tiny Mobilenet v2 128 3x 3.52M 23.2MB 1024×1024 35.89 10.68 73.93 Wait rtr
Comments
  • 论文结果

    论文结果

    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)]

    opened by bestzsq 4
  • hrsc to coco format

    hrsc to coco format

    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?

    opened by geobao 3
  • Trying to replicate an experiment

    Trying to replicate an experiment

    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

    opened by geobao 3
  • onnx

    onnx

    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? T65(GLTNU(~Q`K3EHDLNIXT

    opened by yzk-lab 3
  • DOTA test results,what's the difference between '*' and '**'?

    DOTA test results,what's the difference between '*' and '**'?

    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!

    opened by bestzsq 1
  • error of DOTA_devkit/prepare_dota.py

    error of DOTA_devkit/prepare_dota.py

    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'

    opened by gys1287009045 1
  • Where is Multi-level sampling strategy (MLS)  in the code?

    Where is Multi-level sampling strategy (MLS) in the code?

    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!

    opened by youranran 1
  • Head头内等函数疑惑

    Head头内等函数疑惑

    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!

    opened by chentp-1183 0
  • 卡住不动,训练不了

    卡住不动,训练不了

    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!

    opened by longzeyilang 3
  • what is the argument regress_weight for?

    what is the argument regress_weight for?

    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

    opened by geobao 0
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