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CLI Commands

predict command usage:

>>sahi predict --source image/file/or/folder --model_path path/to/model --model_config_path path/to/config

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

  • It also supports video input:
>>sahi predict --model_path yolov5s.pt --model_type yolov5 --source video.mp4

You can also view video render during video inference with --view_video:

>>sahi predict --model_path yolov5s.pt --model_type yolov5 --source video.mp4 --view_video
  • To forward 100 frames, on opened window press key D
  • To revert 100 frames, on opened window press key A
  • To forward 20 frames, on opened window press key G
  • To revert 20 frames, on opened window press key F
  • To exit, on opened window press key Esc

Note: If --view_video is slow, you can add --frame_skip_interval=20 argument to skip interval of 20 frames each time.

You can specify additional sliced prediction parameters as:

>>sahi predict --slice_width 512 --slice_height 512 --overlap_height_ratio 0.1 --overlap_width_ratio 0.1 --model_confidence_threshold 0.25 --source image/file/or/folder --model_path path/to/model --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 GREEDYNMM or --postprocess_type NMS to be applied over sliced predictions

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

  • Specify postprocess match threshold as --postprocess_match_threshold 0.5

  • Add --postprocess_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 --export_pickle and --export_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_prediction argument. If you don't want to perform standard prediction add --no_standard_prediction argument.

  • If you want to perform prediction using a COCO annotation file, provide COCO json path as add --dataset_json_path dataset.json 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 command to calculate COCO evaluation results or coco_error_analysis command to calculate detailed COCO error plots.

predict-fiftyone command usage:

>>sahi predict-fiftyone --image_dir image/file/or/folder --dataset_json_path dataset.json --model_path path/to/model --model_config_path path/to/config

will perform sliced inference on default parameters and show the inference result on FiftyOne App.

You can specify additional all extra parameters of the sahi predict command.

coco fiftyone command usage:

You need to convert your predictions into COCO result json, sahi predict command can be used to create that.

>>sahi coco fiftyone --image_dir dir/to/images --dataset_json_path dataset.json cocoresult1.json cocoresult2.json

will open a FiftyOne app that visualizes the given dataset and 2 detection results.

Specify IOU threshold for FP/TP by --iou_threshold 0.5 argument

coco slice command usage:

>>sahi coco slice --image_dir dir/to/images --dataset_json_path dataset.json

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.

coco yolov5 command usage:

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

>>sahi coco yolov5 --image_dir dir/to/images --dataset_json_path dataset.json  --train_split 0.9

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

coco evaluate command usage:

You need to convert your predictions into COCO result json, sahi predict command can be used to create that.

>>sahi coco evaluate --dataset_json_path dataset.json --result_json_path result.json

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

If you want to specify mAP metric type, set it as --type bbox or --type 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 analyse command usage:

You need to convert your predictions into COCO result json, sahi predict command can be used to create that.

>>sahi coco analyse --dataset_json_path dataset.json --result_json_path result.json --out_dir output/directory

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

If you want to specify mAP result type, set it as --type bbox or --type segm.

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]".

env command usage:

Print related package versions in the current env as:

>>sahi env
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   torch version 1.11.0 is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   torchvision version 0.12.0 is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   yolov5 version 6.1.3 is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   mmdet version 2.25.0 is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   mmcv version 1.5.3 is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   detectron2 version 0.6+cpu is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   transformers version 4.20.0 is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   timm version 0.4.12 is available.
06/19/2022 21:24:52 - INFO - sahi.utils.import_utils -   fiftyone version 0.14.2 is available.

version command usage:

Print your SAHI verison as:

>>sahi version
0.10.0

Custom scripts

All scripts can be downloaded from scripts directory and modified by your needs. After installing sahi by pip, all scripts can be called from any directory as:

python script_name.py