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Datasets:
joonhaim
/
surgical-tool-recognition-full-multiview

Tasks:
Object Detection
Modalities:
Image
Text
Formats:
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Languages:
English
Size:
1K - 10K
Tags:
medical
computer-vision
surgery
object-detection
yolo
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End of preview. Expand in Data Studio

Surgical Tool Recognition Full Multiview

Summary

This dataset contains images of individual surgical instruments for object detection.
It was originally created in YOLO format and exported here to a Hugging Face-friendly structure with metadata.jsonl files for each split.

Splits

  • train: 2016
  • validation: 252
  • test: 252

Total: 2520 images

Classes

  • 0 = clamp
  • 1 = needle_holder
  • 2 = scalpel
  • 3 = shear
  • 4 = tweezer

File naming convention

Each image filename follows:

{TOOL}_{VIEW}_{BACKGROUND}_{STATE}_{index}

Example:

CM_CLO_BL_CL_003.jpg

Tool codes

  • CM = clamp
  • NH = needle holder
  • SC = scalpel
  • SH = shear
  • TW = tweezer

View codes

  • CLO = close view
  • OBL = oblique view
  • TOP = top view

Background codes

  • BL = blue
  • WH = white
  • TR = metal tray
  • GR = green

State codes

  • OP = open
  • CL = closed
  • NA = not applicable

Hinged instruments (CM, NH, SH) use OP and CL.
Non-hinged instruments (SC, TW) use NA.

Structure

  • train/images/ + train/metadata.jsonl
  • validation/images/ + validation/metadata.jsonl
  • test/images/ + test/metadata.jsonl

Annotation format

The original annotations were in YOLO format:

class_id x_center y_center width height

In this export, annotations are stored in metadata.jsonl with:

  • file_name
  • width
  • height
  • stem
  • split
  • objects.bbox
  • objects.categories
  • objects.category_names

Bounding boxes are stored as:

[x_min, y_min, width, height]

Notes

  • One instrument per image
  • One bounding box per image
  • Controlled viewpoints and backgrounds
  • Intended for research and educational use in surgical computer vision
Downloads last month
30
Size of the auto-converted Parquet files (First 5GB per split):
6.83 GB
Number of rows (First 5GB per split):
2,204

Models trained or fine-tuned on joonhaim/surgical-tool-recognition-full-multiview

Collection including joonhaim/surgical-tool-recognition-full-multiview