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Check out the documentation for more information.
π Automotive Hand Tools Instance Segmentation Demo
Real-World Computer Vision Dataset
40 carefully selected images from a commercial dataset containing 579 real automotive workshop photographs.
This demo showcases the quality of the images and polygon annotations included in the full commercial dataset.
Perfect for evaluating real-world instance segmentation data before licensing the complete collection.
.png)
Dataset Highlights
- πΈ 100% original photography
- βοΈ 100% manually created polygon annotations
- π« No AI-generated images
- π« No web-scraped images
- π― Pixel-accurate instance segmentation
- π οΈ Real automotive workshop tools
- πΌ Commercial license available
- π Easily convertible to COCO, CVAT, Pascal VOC, LabelMe, and other formats
.png)
Dataset Statistics
| Property | Value |
|---|---|
| Task | Instance Segmentation |
| Annotation Format | YOLOv8 Segmentation |
| Images | 579 (Version 1.0) |
| Object Classes | 37 |
| Annotation Type | Polygon Segmentation |
| Image Resolution | High Resolution |
| Photography | Original |
| License | Commercial |
Object Classes
| ID | Class |
|---|---|
| 0 | wrench_combination |
| 1 | wrench_ratchet |
| 2 | breaker_bar |
| 3 | socket |
| 4 | socket_extension |
| 5 | pliers |
| 6 | wire_cutters |
| 7 | screwdriver |
| 8 | hammer |
| 9 | fastener |
| 10 | clip_removal_tool |
| 11 | chisel |
| 12 | PPE_safety_goggles |
| 13 | tool_case |
| 14 | long_nose_pliers |
| 15 | PPE_safety_gloves |
| 16 | lug_wrench |
| 17 | metal_shears |
| 18 | panel_removal_tool |
| 19 | angle_grinder |
| 20 | pincers |
| 21 | oil_dispenser |
| 22 | magnetic_pickup_tool |
| 23 | hose_clamp_pliers |
| 24 | band_strapping_tool |
| 25 | telescopic_tram_gauge |
| 26 | adjustable_wrench |
| 27 | bearing_puller |
| 28 | pry_bar |
| 29 | utility_knife |
| 30 | caliper |
| 31 | hose_clamp |
| 32 | hex_key |
| 33 | diagnostic_tool |
| 34 | torque_wrench |
| 35 | oil_filter_wrench |
| 36 | bar_clamp |
Image Diversity
The dataset intentionally includes difficult real-world conditions commonly encountered in production environments.
Images include:
- Clean tools
- Oily tools
- Rusty tools
- Dirty surfaces
- Strong reflections
- Motion blur
- Bright sunlight
- Low-light conditions
- Indoor workshop lighting
- Outdoor environments
- Partial occlusions
- Objects held in hand
- Multiple overlapping objects
- Various viewing angles
- Different object scales
- Complex backgrounds
These challenging scenarios improve model robustness compared to datasets containing only clean studio images.
Annotation Quality
Every image was manually annotated using polygon segmentation.
Annotations were created with high attention to boundary accuracy and consistency.
Each object instance was individually segmented to provide precise training data suitable for modern segmentation architectures, including:
- YOLOv8-seg
- YOLOv11-seg
- Mask R-CNN
- Detectron2
- MMDetection
- Segment Anything (SAM) fine-tuning
Dataset Structure
Automotive_Hand_Tools_Instance_Segmentation/
βββ README.md
βββ LICENSE.md
βββ data.yaml
βββ classes.txt
βββ metadata.xlsx
β
βββ images/
β βββ train/
β βββ val/
β
βββ labels/
β βββ train/
β βββ val/
β
βββ preview/
β βββ original/
β βββ overlays/
β βββ preview.pdf
β
βββ scripts/
Annotation Format
The dataset uses the Ultralytics YOLOv8 segmentation format.
Each annotation file contains one object instance per line.
<class_id> <x1> <y1> <x2> <y2> ... <xn> <yn>
where:
class_idβ object category IDxandyβ normalized polygon coordinates
Typical Applications
This dataset is suitable for:
- Instance Segmentation
- Object Detection
- Industrial Automation
- Robotics
- Automated Inventory Management
- Smart Workshops
- Predictive Maintenance
- Tool Tracking
- Manufacturing AI
- Warehouse Automation
- Computer Vision Research
Why This Dataset?
Unlike many public datasets, this collection was created specifically for commercial AI development.
Key advantages include:
- Original photography created exclusively for this dataset
- No copyright concerns associated with scraped web images
- Challenging real-world conditions instead of ideal studio scenes
- High-quality manual polygon annotations
- Commercial licensing
- Suitable for production AI systems
Future Development
The dataset will continue to expand.
Planned improvements include:
- Approximately 1,000 original images
- Additional tool categories
- More challenging industrial scenarios
- Increased object diversity
- Continuous annotation quality improvements
License
This dataset is distributed under a Commercial License.
Please refer to LICENSE.md for the complete license terms.
Contact
Ekaterina Bocharova
For licensing inquiries, custom dataset development, enterprise licensing, or commercial collaboration, please get in touch.
www.linkedin.com/in/Π΅ΠΊΠ°ΡΠ΅ΡΠΈΠ½Π°-Π±ΠΎΡΠ°ΡΠΎΠ²Π°-0414a1416
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