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Classification, Object detection, Segmentation MOduleS. Such as automatic format conversion, visualization, metrics with plotting and exporting suspected data.

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COSMOduleS: Classification, Object detection, Segmentation MOduleS



Github: source

Comprehensive preprocessing and post-processing tools for common Computer Vision tasks.

Tasks Subtasks Defined
Format
Visualization Format
Conversion
Metrics Label
Merging
Active
Learning
Classification single-label
single-label-bg
multi-label(binary)
single_label
single_label_bg
multi_label
- - ALL ALL Entropy
Detection - coco
voc
yolo
GENERAL
ALL between ANY
two types
ALL ALL horizontal
flip
Segmentation instance
semantic
coco
GENERAL
ALL coco2general ALL - instance
semantic
  • "bg" means containing background classes which the index must start from 0 in this repo.

Quick start

  • SDK

    pip install -e .
  • Debug

    docker compose build
    docker compose up -d

Motivation

  • Classification
task data format compute class-0 metrics threshold optimization
multi-class classification single label V
anomaly classification single label background V (fore-back)
multi-label classification multi_label V V (mean)
  • Object Detection
    • Develop a GENERAL format to be the most convenient.
    • The formats can be summarized as following:
format extension files type box disadvantage
coco .json 1 int (xmin, ymin, w, h) get label of an image
yolo .txt len(imgs) float (cx, cy, w/2, h/2) visualization, compute metrics, etc.
voc .xml len(imgs) int (xmin, ymin, xmax, ymax) get class list
general .json 1 int (xmin, ymin, xmax, ymax) NO
  • Segmentation
    • Develop a GENERAL format to be the most convenient.
    • prediction format
      • numpy, shape=(num_classes, H, W)
      • value=0~1
Includes Content Advantage
general.json Includes every imgs: path, contour, filled and boxes with class Searching
gt_contour_*.npy (H, W) with {0, 1, ..., num_classes} int Plotting
gt_filled_*.npy (num_classes, H, W) with 0 or 1 int values Compute IOU for Metrics
*.jpg Raw data -

Examples

  • detection visualization .

  • confusion .

  • prf curves .

More

  • Feel free to ask if you have any question.
  • Notice not supported
    • segmentation general2coco
    • segmentation label merging

Acknowledgement

  • Confusion Matrix reference here

About

Classification, Object detection, Segmentation MOduleS. Such as automatic format conversion, visualization, metrics with plotting and exporting suspected data.

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