Tiny Object Detection in Aerial Images.

Related tags

Deep Learning AI-TOD
Overview

AI-TOD

AI-TOD is a dataset for tiny object detection in aerial images.

[Paper] [Dataset]

Description

AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others.

Download

You can download the dataset on Google Driver.

Evaluation

Training and Validation sets are publicly available. If you want to report the accuracies on test set, please send the results on test set to [email protected].

Citation

If you use this dataset in your research, please cite this paper.

@inproceedings{AI-TOD_2020_ICPR,
    title={Tiny Object Detection in Aerial Images},
    author={Wang, Jinwang and Yang, Wen and Guo, Haowen and Zhang, Ruixiang and Xia, Gui-Song},
    booktitle=ICPR,
    pages={3791--3798},
    year={2021},
}
Comments
  • About the annotation files of AI-TOD

    About the annotation files of AI-TOD

    Hi,

    I follow the orignal instruction and aitodtoolkit and I get some annotation files, but I find the numbers of instances are not consistent as reported in AI-TOD paper. So I want to know if there are any processed annotation files that can be used directly.

    I listed the results as following: image

    My results:

    category_id | aitod_train.json | aitod_val.json | aitod_trainval.json | aitod_test.json -- | -- | -- | -- | -- 1 | 623 | 170 | 793 | 745 2 | 512 | 140 | 652 | 689 3 | 5269 | 2477 | 7746 | 5860 4 | 13539 | 3791 | 17330 | 17633 5 | 293 | 34 | 327 | 292 6 | 248051 | 59906 | 307957 | 306678 7 | 14126 | 3841 | 17967 | 15443 8 | 176 | 67 | 243 | 290 total | 282589 | 70426 | 353015 | 347630

    opened by haotianll 2
  • NameError: name 'wasserstein_nms' is not defined

    NameError: name 'wasserstein_nms' is not defined

    你好，我训练atss_r50_aitod_nwd.py时，程序不报错。 但我训练detectors_cascade_rcnn_r50_aitod_rpn_nwd.py时，程序报错NameError: name 'wasserstein_nms' is not defined，请问是怎么回事呢？ 环境如下： Python: 3.7.15 (default, Nov 24 2022, 21:12:53) [GCC 11.2.0] CUDA available: True GPU 0,1: Tesla V100-SXM2-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.1, V10.1.105 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.5.0+cu101 PyTorch compiling details: PyTorch built with:

    • GCC 7.3
    • C++ Version: 201402
    • Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
    • Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
    • OpenMP 201511 (a.k.a. OpenMP 4.5)
    • NNPACK is enabled
    • CPU capability usage: AVX2
    • CUDA Runtime 10.1
    • 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
    • CuDNN 7.6.3
    • Magma 2.5.2 TorchVision: 0.6.0+cu101 OpenCV: 4.6.0 MMCV: 1.3.5 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.1 MMDetection: 2.13.0+9775ac2
    opened by Cathy1900 0
  • AP未达到论文中报告的结果

    AP未达到论文中报告的结果

    你好，我在AI-TOD的test set上运行了detectors_cascade_rcnn_r50_aitod_rpn_nwd.py，但性能未达到论文中报告的精度（20.8 AP）。 我对detectors_cascade_rcnn_r50_aitod_rpn_nwd.py进行了以下修改： 1）在训练时，将detectors_cascade_rcnn_r50_aitod_rpn_nwd修改为在两个GPU上运行，每个GPU上运行4张图片，保持batch size为8。

    2）在推理时，修改用于推理的图像和label路径： ann_file='data/AI-TOD/annotations/aitod_test_v1_1.0.json', img_prefix='data/AI-TOD/test/',

    使用以下指令得到test set的性能： python tools/test.py work_dirs/nwd/detectors_cascade_rcnn_r50_aitod_rpn_nwd/detectors_cascade_rcnn_r50_aitod_rpn_nwd.py work_dirs/nwd/detectors_cascade_rcnn_r50_aitod_rpn_nwd/epoch_12.pth --eval bbox

    3）具体的AP性能如下： [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 14018/14018, 2.3 task/s, elapsed: 6169s, ETA: 0s Evaluating bbox... Loading and preparing results... DONE (t=6.76s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=2665.67s). Accumulating evaluation results... DONE (t=29.74s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=1500 ] = 0.187 Average Precision (AP) @[ IoU=0.25 | area= all | maxDets=1500 ] = -1.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1500 ] = 0.451 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1500 ] = 0.126 Average Precision (AP) @[ IoU=0.50:0.95 | area=verytiny | maxDets=1500 ] = 0.041 Average Precision (AP) @[ IoU=0.50:0.95 | area= tiny | maxDets=1500 ] = 0.175 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1500 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1500 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1500 ] = 0.303 Average Recall (AR) @[ IoU=0.50:0.95 | area=verytiny | maxDets=1500 ] = 0.054 Average Recall (AR) @[ IoU=0.50:0.95 | area= tiny | maxDets=1500 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1500 ] = 0.383 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1500 ] = 0.424 Optimal LRP @[ IoU=0.50 | area= all | maxDets=1500 ] = -1.000 Optimal LRP Loc @[ IoU=0.50 | area= all | maxDets=1500 ] = -1.000 Optimal LRP FP @[ IoU=0.50 | area= all | maxDets=1500 ] = -1.000 Optimal LRP FN @[ IoU=0.50 | area= all | maxDets=1500 ] = -1.000 #Class-specific LRP-Optimal Thresholds # [-1. -1. -1. -1. -1. -1. -1. -1.]

    4）配置环境如下： Python: 3.7.15 (default, Nov 24 2022, 21:12:53) [GCC 11.2.0] CUDA available: True GPU 0,1: NVIDIA A100 80GB PCIe CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.1.TC455_06.29069683_0 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 PyTorch: 1.10.0+cu111 PyTorch compiling details: PyTorch built with:

    • GCC 7.3
    • C++ Version: 201402
    • Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
    • Intel(R) 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: AVX512
    • 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, TorchVision: 0.11.0+cu111 OpenCV: 4.6.0 MMCV: 1.3.5 MMCV Compiler: GCC 9.4 MMCV CUDA Compiler: 11.1 MMDetection: 2.13.0+

    请问产生这种情况的原因是什么？期待您的回复，谢谢！

    opened by Cathy1900 4
  • AI-TOD的标签

    AI-TOD的标签

    你好，请问config文件中提到的aitod_training_v1.0.json和aitod_validation_v1.0.json在哪里下载？ 我把complete_annotations解压后，没有找到aitod_training_v1.0.json和aitod_validation_v1.0.json。 谢谢，期待您的回复。

    opened by Cathy1900 2
  • Error in tiff images while executing python generate_aitod_imgs.py.

    Error in tiff images while executing python generate_aitod_imgs.py.

    Hello Sir,

    I followed the steps as mentioned, and go to the execution part of "python generate_aitod_imgs.py". However, in my terminal, the error depicted in the below given image is being shown. Please do let me know of a way in which I can rectify it and whether the error is in some of the tiff images only or something else. Thanks.

    Screenshoterror

    opened by Pranav051100 21
  • Not able to find the xview_train.geojson file

    Not able to find the xview_train.geojson file

    Hey sir,

    Could you please help me with the .geojson file? I am not able to understand from where to download it. I have downloaded the rest of the files and images and arranged them as mentioned by am stuck with just this one. Any help would be useful.

    opened by Pranav051100 3
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