hinged-state-classifier-crops

Binary image classification dataset for open vs. closed state recognition of hinged surgical instruments.

Overview

This dataset contains cropped images of three hinged surgical tool categories:

• CM = clamp
• NH = needle holder
• SH = shear

The target classes are:

• open
• closed

It is intended for training and evaluating hinge-state classifiers in a two-stage surgical tool recognition pipeline, where an instrument is first detected and then classified as open or closed.

Structure

hinged_state_classifier_crops/
├── train/
│   ├── open/
│   └── closed/
├── val/
│   ├── open/
│   └── closed/
└── test/
    ├── open/
    └── closed/

Dataset size

Total images: 1512

Split	Open	Closed	Total
Train	603	606	1209
Val	75	71	146
Test	78	79	157
Total	756	756	1512

The dataset is fully balanced at the class level.

Global distribution

By tool

Tool	Count
CM	504
NH	504
SH	504

By view

View	Count
CLO	504
OBL	504
TOP	504

By background

Background	Count
BL	378
GR	378
TR	378
WH	378

Filename convention

Images follow this naming pattern:

{TOOL}_{VIEW}_{BACKGROUND}_{STATE}_{INDEX}

Where:

• TOOL: CM, NH, SH
• VIEW: CLO, OBL, TOP
• BACKGROUND: BL, GR, TR, WH
• STATE: OP, CL

Validation

The dataset passed validation successfully:

• Expected total images: 1512
• Observed total images: 1512
• Problems found: 0
• Warnings found: 0
• Duplicate image-content groups: 0
• Cross-split duplicate image-content groups: 0

Intended use

This dataset is intended for:

• binary image classification of hinged surgical instrument state,
• benchmarking open/closed classifiers,
• evaluating robustness across viewpoints and backgrounds,
• use in multi-stage surgical tool recognition systems.