fcakyon / small-object-detection-benchmark

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

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small-object-detection-benchmark

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

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 request
FCOS+SF+SAHI+FI+PO 38.5 25.9 55.4 59.8 download request
--- --- --- --- --- --- ---
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 request
FCOS+SF+SAHI+PO 17.1 12.2 20.2 12.8 download request
FCOS+SF+SAHI+FI 15.7 11.9 18.4 14.3 download request
FCOS+SF+SAHI+FI+PO 17.0 12.2 20.2 15.8 download request
--- --- --- --- --- --- ---
VFNet+FI 2.10 0.50 1.80 6.80 download request
VFNet+SF+SAHI 16.0 11.9 17.6 13.1 download request
VFNet+SF+SAHI+PO 17.7 13.7 19.7 15.4 download request
VFNet+SF+SAHI+FI 15.8 11.9 17.5 15.2 download request
VFNet+SF+SAHI+FI+PO 17.5 13.7 19.6 17.6 download request
--- --- --- --- --- --- ---
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:

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

install other requirements:

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 predict_evaluate_analyse script then run the script.

roadmap

  • 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
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sahi benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors

License:MIT License


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