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

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

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

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

install pytorch:

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

install 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_SETTING in 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