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SCRIPTS.md

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SCRIPTS

predict.py script usage:

python scripts/predict.py --source image/file/or/folder --model_path path/to/model --config_path path/to/config

will perform sliced inference on default parameters and export the prediction visuals to runs/predict/exp folder.

You can specify additional sliced prediction parameters as:

python scripts/predict.py --slice_width 256 --slice_height 256 --overlap_height_ratio 0.1 --overlap_width_ratio 0.1 --conf_thresh 0.25 --source image/file/or/folder --model_path path/to/model --config_path path/to/config

  • Specify detection framework as --model_type mmdet for MMDetection or --model_type yolov5 for YOLOv5, to match with your model weight

  • Specify postprocess type as --postprocess_type UNIONMERGE or --postprocess_type NMS to be applied over sliced predictions

  • Specify postprocess match metric as --match_metric IOS for intersection over smaller area or --match_metric IOU for intersection over union

  • Specify postprocess match threshold as --match_thresh 0.5

  • Add --class_agnostic argument to ignore category ids of the predictions during postprocess (merging/nms)

  • If you want to export prediction pickles and cropped predictions add --pickle and --crop arguments. If you want to change crop extension type, set it as --visual_export_format JPG.

  • If you don't want to export prediction visuals, add --novisual argument.

  • By default, scripts apply both standard and sliced prediction (multi-stage inference). If you don't want to perform sliced prediction add --no_sliced_pred argument. If you don't want to perform standard prediction add --no_standard_pred argument.

  • If you want to perform prediction using a COCO annotation file, provide COCO json path as add --coco_file path/to/coco/file and coco image folder as --source path/to/coco/image/folder, predictions will be exported as a coco json file to runs/predict/exp/results.json. Then you can use coco_evaluation.py script to calculate COCO evaluation results or coco_error_analysis.py script to calculate detailed COCO error plots.

slice_coco.py script usage:

python scripts/slice_coco.py path/to/coco/json/file coco/images/directory

will slice the given images and COCO formatted annotations and export them to given output folder directory.

Specify slice height/width size as --slice_size 512.

Specify slice overlap ratio for height/width size as --overlap_ratio 0.2.

If you want to ignore images with annotations set it add --ignore_negative_samples argument.

coco2yolov5.py script usage:

(In Windows be sure to open anaconda cmd prompt/windows cmd as admin to be able to create symlinks properly.)

python scripts/coco2yolov5.py --coco_file path/to/coco/file --source coco/images/directory --train_split 0.9

will convert given coco dataset to yolov5 format and export to runs/coco2yolov5/exp folder.

coco_evaluation.py script usage:

python scripts/coco_evaluation.py dataset.json results.json

will calculate coco evaluation and export them to given output folder directory.

If you want to specify mAP metric type, set it as --metric bbox mask.

If you want to also calculate classwise scores add --classwise argument.

If you want to specify max detections, set it as --proposal_nums 10 100 500.

If you want to specify a psecific IOU threshold, set it as --iou_thrs 0.5. Default includes 0.50:0.95 and 0.5 scores.

If you want to specify export directory, set it as --out_dir output/folder/directory .

coco_error_analysis.py script usage:

python scripts/coco_error_analysis.py dataset.json results.json

will calculate coco error plots and export them to given output folder directory.

If you want to specify mAP result type, set it as --types bbox mask.

If you want to export extra mAP bar plots and annotation area stats add --extraplots argument.

If you want to specify area regions, set it as --areas 1024 9216 10000000000.

If you want to specify export directory, set it as --out_dir output/folder/directory .