---
license: mit
dataset_info:
  features:
  - name: guide_id
    dtype: int64
  - name: task_title
    dtype: string
  - name: device_name
    dtype: string
  - name: difficulty
    dtype: string
  - name: tools
    list: string
  - name: time_required_min
    dtype: int64
  - name: time_required_max
    dtype: int64
  - name: image_path
    dtype: image
  - name: text
    dtype: string
  - name: type
    dtype: string
  splits:
  - name: train
    num_bytes: 105039306.48
    num_examples: 4495
  download_size: 97535095
  dataset_size: 105039306.48
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---


#  Electronics Repair Dataset

**A comprehensive mini dataset covering smartwatch and other wearable repair and teardown procedures.**

![Dataset Preview](https://img.shields.io/badge/Examples-4,649-blue) ![Images](https://img.shields.io/badge/Images-4,649-green) ![Categories](https://img.shields.io/badge/Categories-3-orange)

##  Dataset Overview

This dataset contains **4,649 carefully examples** 

##  Dataset Structure

Each example contains rich metadata and high-quality repair images:

```python
{
    'guide_id': 37170,
    'task_title': 'Microsoft Band Wrist Clasp Replacement',
    'device_name': 'Microsoft Band', 
    'difficulty': 'Easy',
    'tools': ['T3 Torx Screwdriver'],
    'time_required_min': 300,
    'time_required_max': 600,
    'image_path': <PIL.Image(512, 512)>,  # High-quality repair image
    'text': 'This is a Microsoft Band Wrist Clasp Replacement guide...',
    'type': 'guide_overview'  # or 'step_instruction', 'teardown_analysis'
}
```

## Quick Start

```python
from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("ankithreddy/repairdataset-mini")

# Access examples
example = dataset['train'][0]
image = example['image_path']  # PIL Image object
instruction = example['text']  # Repair instruction text
device = example['device_name']  # Target device

# Filter by category
repair_guides = dataset['train'].filter(lambda x: x['type'] == 'guide_overview')
teardowns = dataset['train'].filter(lambda x: x['type'] == 'teardown_analysis')
steps = dataset['train'].filter(lambda x: x['type'] == 'step_instruction')
```

This dataset was created using publicly available data from here https://www.ifixit.com/api/2.0/doc for research and educational purposes only. All data belongs to iFixit and respective contributors