Short answer
Tool identification image data labels what a tool is: its type, size, drive and sometimes make, in images from catalogs, tool rooms and workshops. It trains a humanoid's perception to find and fetch the right tool and to tell look-alikes apart. Catalog images give clean coverage; in-situ photos add clutter, wear and storage. Recognizing a tool shows nothing about how to hold or use it, and manufacturer-supplied images carry rights limits.
Why recognition comes before use
A maintenance humanoid that is asked for "the 13 mm socket and a ratchet" has to find them in a drawer, on a pegboard or in a cart before any fastening starts. Fetching the wrong size is not a harmless error: it can round a fastener head or fail to engage. Identification is therefore a perception task with real consequences, and it can be bought and evaluated as images alone.
This page owns the image side. How a tool is handled is covered in hand tool use demonstrations, where it is held in tool affordances and grasp annotations, how it is tracked in video in tool pose tracking, and which tool suits which fastener in tool selection knowledge. Wear and damage have their own page, tool condition recognition.
What "identified" has to mean
Decide the label depth before you source, because it determines which images are usable.
| Level | Example label | What makes it hard |
|---|---|---|
| Family | Hand tool, power tool, measuring tool | Easy; rarely sufficient |
| Type | Slotted vs Phillips screwdriver; open-end vs ring wrench | Small shape differences at a distance |
| Size or specification | 10 mm vs 13 mm; metric vs imperial; drive size | Often distinguishable only by a stamped marking or by scale |
| Variant | Ratcheting, insulated, torque-limiting | Features hidden by pose or grip |
| Make and model | Catalog SKU | Needed for cross-reference to parts and manuals; product lines change |
| Instance | This physical tool, by asset tag | Tool-crib tracking; needs per-instance identifiers |
Size is the trap. Two wrenches that differ by a few millimeters look alike from a head-mounted camera, so a size label is only trustworthy if the stamp is legible or the label came from an inventory record rather than from the picture. Capture which one it was.
What public data covers
The public sets cited here cover tools as objects or in use, not as a fine-grained identification problem.
- HANDAL holds 308k annotated frames from 2.2k videos of 212 real-world objects in 17 categories, focused on manipulable hardware and kitchen tools such as pliers, utensils and screwdrivers. It annotates category-level 6-DoF pose and scale, handle affordance segmentation and 3D reconstructed meshes, from a single off-the-shelf camera with semi-automated processing (Guo et al., 2023). Seventeen categories is coarse next to size and drive subtypes, so subtype-level identification goes beyond it.
- TACO records 2.5K bimanual motion sequences across tool-action-object combinations, with 12 third-person views plus one egocentric view and hand-object annotations (Liu et al., 2024). It is built for tool use and is covered under demonstrations, not identification.
- T-LESS offers 30 industry-relevant texture-less objects, many symmetric or mutually similar, captured with three synchronized sensors (Hodan et al., 2017). It is not a tool dataset, but it shows the difficulty profile: near-identical metal objects, where per-object 3D models and consistent capture matter.
Buyers who need subtype and size coverage across brands, in images that look like a real workshop, will generally have to source it.
Where tool images come from
| Source | Typical images | Strength | Watch for |
|---|---|---|---|
| Tool makers | Product photography, drawings | Canonical views, accurate specifications | Makers own the images; no clutter, wear or storage context |
| Industrial and automotive distributors | Catalog images with attributes and cross-references | Brand and size breadth; structured attributes | Images often supplied by makers; distributor terms may not extend to model training; see industrial distributor catalogs |
| Repair shops, tool cribs and workshops | Check-out records, shadow-board and drawer photos, bench shots | Real storage, mixed brands, wear | Sparse; people and customers' property in frame; labels may be missing |
| Training providers and vocational shops | Tool-set photos in coursework materials | Clean teaching sets with names | Course content copyright |
| Commissioned capture | Purpose-built sets in realistic arrangements | Designed coverage and labels | Cost, site access, and the need for a photography spec such as multi-view product photography |
Catalogs give breadth and ready-made attributes. Workshops give the domain match. A common plan is catalog images for coverage and in-situ images for fine-tuning and evaluation; packshots vs in-situ images covers the domain gap. For the storage side of retrieval tasks, see tool storage and shadow boards.
Illustrative package
# Illustrative only: synthetic classes, ids and sources, not an available dataset
package: tool_identification_images_v0
sources:
catalog: distributor_A (manufacturer images; terms attached per image)
in_situ: workshop_B tool-crib photos, 2 sites
taxonomy:
levels: family > type > size_spec
mapped_to: UNSPSC commodity codes (crosswalk file)
example: hand_tool > wrench_open_end > 13mm_metric
images:
catalog: white background, multiple views per item, original resolution kept
in_situ: drawer, pegboard, bench, in-hand; oblique and top-down; mixed lighting
annotations:
instance_mask: in-situ images
size_legible: yes | partial | no # can a person read the stamp here?
size_label_source: image_read | inventory_record | catalog_attribute
occlusion: none | partial | heavy
instance_id: tool_inst_0417 # same physical tool across images
metadata: site_id, camera_type, viewpoint, lighting_note, image_owner, license_ref
splits: eval holds out tool instances and one site
excluded: tool use, grasp, pose, force
rights: training and internal evaluation; manufacturer-image terms attached
Class lists, resolutions and split rules are yours to set. A shared classification keeps labels aligned across sources: UNSPSC, for example, uses eight-digit codes across four levels, Segment, Family, Class and Commodity (UNDP), so a distributor's categories and a workshop's can be crosswalked to the same codes.
What tool images cannot support
- Using the tool. An image of a torque wrench does not show how it clicks, how much force a fastener takes or how to brace in a tight space. That is demonstration, instrumented or robot data.
- Grasping and handover. Identity does not give a grasp point or an approach direction. See object recognition vs manipulation data for the general boundary.
- Choosing the tool. Knowing a tool is a 13 mm wrench does not tell the robot that the fastener needs it.
- Wear. Catalog images show new tools. Worn, rusted or damaged tools need their own labels.
- Catalog drift. Brands change designs and part numbers; an old catalog mislabels new product.
Quality and acceptance criteria
- Taxonomy. The label hierarchy is written down and crosswalked to a shared classification.
- Size labels. The share with legible size markings and the source of each size label are reported; a sample is checked against physical tools or inventory records.
- Balance. Counts per subtype are reported; rare subtypes are flagged rather than hidden in an average.
- Instance-level splits. The same physical tool, and near-duplicate catalog views of one product, never sit on both sides of a split. Novel-instance testing is covered in novel-object generalization evaluation.
- Context coverage. Counts by storage context, viewpoint, lighting and background.
- Agreement. A double-labeled sample shows agreement per taxonomy level, not only at family level.
- Resolution. Images are large enough to read stamps at the distances your camera will see.
- Provenance. Each image carries its source, owner, date and license reference.
Questions to ask suppliers
- Who took each image set, and who owns it: the tool maker, you or a third party?
- Do your agreements with makers allow sublicensing images for model training?
- How are size and drive attributes recorded, and are they checked against the physical tool?
- Do you hold photos of tools in storage, at check-out or in use, not only product shots?
- How are individual tools identified across images: asset tag, serial number or SKU?
- Which brands and tool families are covered, and how current is the catalog?
- Are people, customers' vehicles or equipment, or job details visible in workshop images?
- Can images be delivered at original resolution with their metadata?
Permissions and provenance
Product photographs are authored works, and a distributor that displays a maker's image may not hold the right to license it onward. Logos and brand marks appear on many tools. Workshop photos can show employees, customers' property and job details. Rights in product images and third-party content in workplace recordings cover the questions in depth. Blurring a face or a plate does not settle whether an image may be licensed. This is not legal advice.
Discuss your requirements
If your robot has to find and fetch tools, describe the tool families, label depth and image sources you need to SourceX. SourceX investigates whether US businesses hold suitable images, checks the data and the supplier's licensing permissions, and coordinates the license and delivery. If no existing asset fits, SourceX can scope a commissioned collection of new recordings. A request does not guarantee a match; SourceX follows up about potential sources.
The maintenance, repair and tool-use hub and the humanoid training data guide place this asset among records and demonstrations. The catalog pages on images and inspection photos and product catalogs and descriptions describe related record types.
Discuss your humanoid training-data requirements with SourceX
Sources
- HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions — hardware and kitchen tools with pose and handle affordance annotations.
- TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding — tool-action-object sequences with multi-view capture.
- T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects — similar, texture-less industrial objects.
- United Nations Standard Products and Services Code (UNSPSC) — shared product classification for crosswalking labels.