🔥 our paper has been presented in ICIP 2022 Bordeaux, France (16-19 October 2022)
📜 List of publications that cite this work (currently 30+)
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
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}
}
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 |
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 |
install pytorch:
conda install pytorch=1.10.0 torchvision=0.11.1 cudatoolkit=11.3 -c pytorchinstall other requirements:
pip install -r requirements.txt-
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_SETTINGin predict_evaluate_analyse script then run the script.
- add train test split support for xview to coco converter
- add mmdet config files (fcos, vfnet and tood) for xview training (9 train experiments)
- add mmdet config files (fcos, vfnet and tood) for visdrone training (9 train experiments)
- add coco result.json files, classwise coco eval results error analysis plots for all xview experiments
- add coco result.json files, classwise coco eval results error analysis plots for all visdrone experiments
- add .py scripts for inference + evaluation + error analysis using
sahi