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small-object-detection-benchmark

icip2022 paper: sahi benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors

https://github.com/fcakyon/small-object-detection-benchmark

Science Score: 67.0%

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    Low similarity (8.3%) to scientific vocabulary

Keywords

benchmark coco fcos icip2022 mmdetection object-detection pytorch sahi small-object-detection tood vfnet visdrone xview
Last synced: 6 months ago · JSON representation ·

Repository

icip2022 paper: sahi benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors

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  • Stars: 176
  • Watchers: 2
  • Forks: 24
  • Open Issues: 0
  • Releases: 2
Topics
benchmark coco fcos icip2022 mmdetection object-detection pytorch sahi small-object-detection tood vfnet visdrone xview
Created about 4 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

small-object-detection-benchmark

ci fcakyon twitter

🔥 our paper has been presented in ICIP 2022 Bordeaux, France (16-19 October 2022)

📜 List of publications that cite this work (currently 300+)

summary

small-object-detection benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors

refer to Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection for full technical analysis

citation

If you use any file/result from this repo in your work, please cite it as:

@article{akyon2022sahi, title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection}, author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin}, journal={2022 IEEE International Conference on Image Processing (ICIP)}, doi={10.1109/ICIP46576.2022.9897990}, pages={966-970}, year={2022} }

visdrone results

refer to table 1 in Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection for more detail on visdrone results

|setup |AP50 |AP50s |AP50m |AP50l | results | checkpoints | |--- |--- |--- |--- |--- |--- |--- | |FCOS+FI |25.8 |14.2 |39.6 |45.1 | download | request | |FCOS+SAHI+PO |29.0 |18.9 |41.5 |46.4 | download | request | |FCOS+SAHI+FI+PO |31.0 |19.8 |44.6 |49.0 | download | request | |FCOS+SF+SAHI+PO |38.1 |25.7 |54.8 |56.9 | download | download | |FCOS+SF+SAHI+FI+PO |38.5 |25.9 |55.4 |59.8 | download | download | |--- |--- |--- |--- |--- |--- |--- | |VFNet+FI |28.8 |16.8 |44.0 |47.5 | download | request | |VFNet+SAHI+PO |32.0 |21.4 |45.8 |45.5 | download | request | |VFNet+SAHI+FI+PO |33.9 |22.4 |49.1 |49.4 | download | request | |VFNet+SF+SAHI+PO |41.9 |29.7 |58.8 |60.6 | download | request | |VFNet+SF+SAHI+FI+PO |42.2 |29.6 |59.2 |63.3 | download | request | |--- |--- |--- |--- |--- |--- |--- | |TOOD+FI |29.4 |18.1 |44.1 |50.0 | download | request | |TOOD+SAHI |31.9 |22.6 |44.0 |45.2 | download | request | |TOOD+SAHI+PO |32.5 |22.8 |45.2 |43.6 | download | request | |TOOD+SAHI+FI |34.6 |23.8 |48.5 |53.1 | download | request | |TOOD+SAHI+FI+PO |34.7 |23.8 |48.9 |50.3| download | request | |TOOD+SF+FI |36.8 |24.4 |53.8 |66.4 | download | download | |TOOD+SF+SAHI |42.5 |31.6 |58.0 |61.1 | download | download | |TOOD+SF+SAHI+PO |43.1 |31.7 |59.0 |60.2 | download | download | |TOOD+SF+SAHI+FI |43.4 |31.7 |59.6 |65.6 | download | download | |TOOD+SF+SAHI+FI+PO |43.5 |31.7 |59.8 |65.4 | download | download |

xview results

refer to table 2 in Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection for more detail on xview results

|setup |AP50 |AP50s |AP50m |AP50l | results | checkpoints | |--- |--- |--- |--- |--- |--- |--- | |FCOS+FI |2.20 |0.10 |1.80 |7.30 | download | request | |FCOS+SF+SAHI |15.8 |11.9 |18.4 |11.0 | download | download | |FCOS+SF+SAHI+PO |17.1 |12.2 |20.2 |12.8 | download | download | |FCOS+SF+SAHI+FI |15.7 |11.9 |18.4 |14.3 | download | download | |FCOS+SF+SAHI+FI+PO |17.0 |12.2 |20.2 |15.8 | download | download | |--- |--- |--- |--- |--- |--- |--- | |VFNet+FI |2.10 |0.50 |1.80 |6.80 | download | request | |VFNet+SF+SAHI | 16.0 |11.9 |17.6 |13.1 | download | download | |VFNet+SF+SAHI+PO |17.7| 13.7 |19.7 |15.4 | download | download | |VFNet+SF+SAHI+FI |15.8 |11.9 |17.5 |15.2 | download | download | |VFNet+SF+SAHI+FI+PO |17.5 |13.7 |19.6 |17.6 | download | download | |--- |--- |--- |--- |--- |--- |--- | |TOOD+FI |2.10 |0.10 |2.00 |5.20 | download | request | |TOOD+SF+SAHI |19.4 |14.6 |22.5 |14.2 | download | download | |TOOD+SF+SAHI+PO |20.6 |14.9 |23.6 |17.0 | download | download | |TOOD+SF+SAHI+FI |19.2 |14.6 |22.3 |14.7 | download | download | |TOOD+SF+SAHI+FI+PO |20.4 |14.9 |23.5 |17.6 | download | download |

env setup

install pytorch:

bash conda install pytorch=1.10.0 torchvision=0.11.1 cudatoolkit=11.3 -c pytorch

install other requirements:

bash pip install -r requirements.txt

evaluation

  • download desired checkpoint from the urls in readme.

  • download xivew or visdrone dataset and convert to COCO format.

  • set MODEL_PATH, MODEL_CONFIG_PATH, EVAL_IMAGES_FOLDER_DIR, EVAL_DATASET_JSON_PATH, INFERENCE_SETTING in predictevaluateanalyse script then run the script.

roadmap

  • [x] add train test split support for xview to coco converter
  • [x] add mmdet config files (fcos, vfnet and tood) for xview training (9 train experiments)
  • [x] add mmdet config files (fcos, vfnet and tood) for visdrone training (9 train experiments)
  • [x] add coco result.json files, classwise coco eval results error analysis plots for all xview experiments
  • [x] add coco result.json files, classwise coco eval results error analysis plots for all visdrone experiments
  • [X] add .py scripts for inference + evaluation + error analysis using sahi

Owner

  • Name: fatih akyon
  • Login: fcakyon
  • Kind: user
  • Location: Ankara, Turkiye
  • Company: @viddexa @ultralytics

helping AI's to understand videos at @ultralytics & @viddexa

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: article
  title: "Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection"
  doi: 10.1109/ICIP46576.2022.9897990
  url: https://ieeexplore.ieee.org/document/9897990
  journal: 2022 IEEE International Conference on Image Processing (ICIP)
  message: "If you use this results in your work, please cite it."
  authors:
  - family-names: "Akyon"
    given-names: "Fatih Cagatay"
  - family-names: "Altinuc"
    given-names: "Sinan Onur"
  - family-names: "Temizel"
    given-names: "Alptekin"
  year: 2022
  start: 966
  end: 970

GitHub Events

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Last Year
  • Watch event: 22
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Committers

Last synced: 9 months ago

All Time
  • Total Commits: 58
  • Total Committers: 2
  • Avg Commits per committer: 29.0
  • Development Distribution Score (DDS): 0.017
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
fcakyon f****n@g****m 57
He Li li@i****m 1
Committer Domains (Top 20 + Academic)
imlihe.com: 1

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 0
  • Total pull requests: 10
  • Average time to close issues: N/A
  • Average time to close pull requests: 27 minutes
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  • Average comments per issue: 0
  • Average comments per pull request: 0.1
  • Merged pull requests: 10
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Past Year
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  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
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  • Average comments per issue: 0
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Top Authors
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  • fcakyon (10)
  • lihe07 (1)
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Packages

  • Total packages: 1
  • Total downloads: unknown
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
proxy.golang.org: github.com/fcakyon/small-object-detection-benchmark
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.4%
Average: 6.7%
Dependent repos count: 6.9%
Last synced: about 1 year ago

Dependencies

requirements.txt pypi
  • mmcv-full ==1.4.3
  • mmdet ==2.21.0
  • pillow *
  • sahi >=0.9.3
  • scipy *
  • tensorboard >=2.7.0
pyproject.toml pypi