amazon/armbench-object-identification

ARMBench - Object Identification

This repository is the official Amazon redistribution, on the Hugging Face Hub, of the object identification subset of ARMBench. The payload under archive/ is Amazon's original release archive, byte-for-byte; nothing in it has been repacked, resharded or re-encoded.

Project home: http://armbench.com  |  Paper: arXiv:2303.16382  |  Contact: armbench-support@amazon.com

Note on the project website. http://armbench.com is Amazon's canonical home for ARMBench and is the URL printed in the paper. At the time of writing the https://www.armbench.com certificate has expired, so some browsers will warn before showing the site. This has been reported to the ARMBench team. The arXiv link above is a non-expiring alternative reference.

Provenance of the quotations in this card. Every block quote below was read from the live ARMBench site on 2026-10-28 and is reproduced verbatim. The quotes come from three different pages, each served with HTTP 200, and are sourced as follows. The Tasks > Sparrow > Identification task page (identification.html) is the source of the task description, the download list and the per-pick layout. The home page (index.html) is the source of the "What is ARMBench?" paragraph, the Sparrow program description, this subset's home-page card, the release history and the Credits licence sentence. The contact page (contactus.html) is the source of the contact line. One block quote is not from the website at all: the paper abstract, which is quoted from arXiv:2303.16382 and labelled as such where it appears. Where this card states a number that is not Amazon's, it is labelled "measured" and the measurement is described. Nothing is paraphrased and attributed to Amazon.

Dataset Description

The following description is quoted verbatim from Amazon's ARMBench website.

What is ARMBench?

ARMBench is a large-scale benchmark dataset for perception and manipulation challenges across Amazon Robotics systems. The datasets are collected in Amazon warehouses and capture a wide variety of objects and configurations. They comprise images, videos, and tabular data for different stages of robotic manipulation with high-quality annotations.

ARMBench currently includes datasets from two Amazon Robotics programs. This subset belongs to the Sparrow Tasks group, described on the site as:

Sparrow - Amazon's intelligent robotic system that streamlines the fulfillment process by moving individual products before they get packaged. Sparrow is a robotic system in Amazon warehouses that can detect, select, and handle individual products in inventory using computer vision and artificial intelligence. It focuses on object segmentation, identification, and defect detection, enabling more efficient and safer operations while allowing employees to focus on higher-value tasks.

ARMBench documents five benchmark tasks and distributes them as six downloads: Defect Detection ships as two separate downloads (images and videos), and the three Object Segmentation subsets ship inside a single download. This repository is one of those six downloads; the others are published under the same amazon organization with the armbench- prefix.

This subset on the ARMBench home page, verbatim

Object Identification: Open-set object recognition challenge with 200,000+ unique objects. Benchmark will evaluate image retrieval and classification methods with uncertainty estimation.

The object identification task, verbatim

Object identification (ID) is the task of exactly identifying an image segment as one of the objects within a database. In the robotic manipulation context, this task is applicable both before and after picking the object. In the pre-pick stage, identifying an object segment within the tote allows accessing any stored models or attributes of the object from past experience which can be used for manipulation planning purposes. In the post-pick stage, ID has access to the segment of the object being manipulated both within the tote as well as when it is attached to the robotic arm.

What the task page says you need, verbatim

To evaluate this usecase, please download the following items:

• Picks: 235,000 pick activities with images of the picked object in tote and in robotic arm.
• Reference-images: Up to 6 images (1.jpg-6.jpg) corresponding to different product-ids.
• Train-Test-Split: List of training and test cases.

All three of those items ship inside the single archive in this repository; there is no separate download to assemble.

The per-pick layout, verbatim

Each pick activity in the pick directory contains the following:

• Image of the object when it was in the container (PickRGB.jpg)
• Images of the object after pick (ToteWallRGB.jpg, OnArmLowRGB.jpg, FarTrayRGB.jpg)
• List of all products in the container before the picking starts. The file contains product-id, text description, weight and dimensions for all the products (container.json)
• Polygon segments for the object in above images, ground-truth product-id for the object (annotation.json)

The only editorial changes in the four quotations above are of layout. The site renders the download list and the per-pick layout as styled list items; they are rendered here as Markdown bullets with the site's own leading labels bolded. The home-page card carries its title, "Object Identification", above the quoted text; it is rendered here as a bolded lead-in label with a colon added. No word is added, removed or reordered.

Dataset Structure

What this repository contains

Path	Bytes	Description
archive/armbench-object-id-0.1.tar.gz	109,039,658,872	Amazon's original release archive, unmodified

That is the payload: one archive plus this card. 109,039,658,872 bytes is 101.551 GiB (109.04 GB), and that is the exact length of Amazon's release object - the stat check further down reproduces it from your own download.

Amazon's 3 MB sample, and why it is not in the table above. The ARMBench release also contains an official sample, armbench-object-id-tiny.tar.gz (3,126,329 bytes), holding 5 complete pick directories. It is a separate object in Amazon's release, so it is present in this repository only if the Files and versions tab lists it. Check that tab before scripting against it; this card deliberately does not assert that it is here. Every structural claim in the sections below was measured from that sample, not from the 101.551 GiB archive.

Why only the tarball, and no extracted file tree

Amazon's own figure is 235,000 pick activities, and each pick directory holds five files (PickRGB.jpg, ToteWallRGB.jpg, OnArmLowRGB.jpg, container.json, annotation.json). That is on the order of 1.2 million files for the picks alone, before the reference images - up to six per product, over a product catalogue the home page puts at 200,000+ unique objects.

Hugging Face's Hub limits apply per folder and per repository: a single folder is capped at 10,000 entries, and the Hub's own guidance is to stay under about 100,000 files per repository. An extracted Picks/ directory would hold ~235,000 entries in one folder - roughly 23x the per-folder cap - and the repository as a whole would be more than an order of magnitude past the recommended file count. So this subset is published exactly as Amazon released it: one gzipped tar, plus the official small sample. Extract locally and the per-pick tree is yours.

A practical consequence: there is no dataset viewer on this repository, and that is deliberate. The viewer needs a Parquet or CSV table, and this subset ships no tabular file. A configs: block pointing at a path that does not exist would break the viewer rather than populate it, so this card does not declare one. To inspect the format, extract the archive - or Amazon's 3 MB sample, if the Files and versions tab lists it.

Archive contents

Measured by listing the official sample archive, armbench-object-id-tiny.tar.gz (83 members including directory entries, 5 picks, listed with tar -tzvf). The sample's root directory is armbench-object-id-tiny/; the layout under it is:

• Picks/<pick_id>/ - one directory per pick activity. <pick_id> is a 21-digit numeric string, e.g. 004017001665774539482.
• Reference_Images/<product_id>/{1.jpg..6.jpg} - the reference gallery, one directory per product id. Product ids are Amazon-style alphanumeric codes, e.g. B0853996GF, X002PQZ5UX. The count of images per product varies and is not always 6, consistent with the site's "up to 6 images".
• train-test-split.pickle - the official split, as a Python pickle (see the warning below).

This card describes the sample's structure because confirming the full archive's member list requires a single sequential pass over a 101.551 GiB non-seekable gzip stream. That pass was not run here, so no claim is made about the full archive's exact member count, and none is implied.

annotation.json schema, measured from the sample

A JSON object. Keys are image filenames plus one label key:

Key	Type	Description
PickRGB.jpg	list of [x, y]	Polygon vertices of the picked object's segment in the in-container image
ToteWallRGB.jpg	list of [x, y]	Polygon vertices of the same object in the tote-wall view
OnArmLowRGB.jpg	list of [x, y]	Polygon vertices of the same object in the on-arm view
GT_ID	string	The ground-truth product id, i.e. the identification target

The polygons are dense: in the sample's five picks the PickRGB.jpg contour runs from 191 to 657 vertices, while the post-pick views are much coarser - 27 to 32 vertices. Expect the in-container segment to be the detailed one.

container.json schema, measured from the sample

A JSON object mapping product id to product record - the manifest of everything in the container before the pick, which is what makes the pre-pick task a closed-set problem within a container and an open-set problem across the dataset:

Key	Type	Description
(object key)	string	Product id, e.g. B008TYX146
name	string	Text description of the product
weight	float	Product weight. Values in the sample are around 0.045-0.9, consistent with kilograms
dimensions.lengthX	float	Product extent, values around 0.1-0.4, consistent with metres
dimensions.lengthY	float	Product extent, same units
dimensions.lengthZ	float	Product extent, same units

The site says container.json contains "product-id, text description, weight and dimensions"; it does not state the units. The ranges above are measured from the sample and the unit reading is an inference, labelled as such. Amazon's site does not publish a units statement for this file.

The train/test split

The split is train-test-split.pickle, a Python pickle at the root of the archive. In the sample archive it is 354 bytes, pickle protocol 4, and - inspected with pickletools.dis, which parses the opcode stream without executing it - a dictionary with exactly two top-level keys:

Key	Type	Sample content
testset	list of strings	The 5 pick ids in the sample
testset-objects	list of strings	14 product ids

Load this file with care. Unpickling executes arbitrary code by design; pickle.load is not a safe reader for a file you downloaded. Inspect it first with pickletools.dis, or load it with a restricted Unpickler that only permits dict, list and str. This is a property of the format Amazon chose, not of this redistribution.

The full archive's split file was not opened here, so this card does not state what keys it carries or how many picks are in each split. The sample contains only test-side keys; do not assume the full file has the same two keys and nothing else.

How the benchmark is framed

The task page calls this "exactly identifying an image segment as one of the objects within a database", and the home page calls it an "open-set object recognition challenge ... with uncertainty estimation". The pieces the archive gives you map onto that directly: the query is a polygon-cropped segment from PickRGB.jpg (pre-pick) or from ToteWallRGB.jpg / OnArmLowRGB.jpg (post-pick); the gallery is Reference_Images/<product_id>/; the target is annotation.json's GT_ID. Amazon's site does not publish a scoring script, a metric definition or a baseline checkpoint for this subset, and none is added here.

Published figures versus measured figures

Amazon's published figures are given first; figures measured from this exact release follow, labelled as such. Both are reported rather than silently reconciled.

Quantity	Amazon's published figure	Measured here
Pick activities	235,000 (task page); "235K+ pick-and-place activities" (paper abstract)	Not counted. Counting requires a full sequential pass over the 101.551 GiB gzip stream, which was not run.
Unique objects	200,000+ (home page); "190K+ unique objects" (paper abstract)	Not counted. The two published figures differ from each other; the home page figure is the newer of the two.
Download size	Not stated on the task page	109,039,658,872 bytes = 101.551 GiB = 109.04 GB, from the release object's exact byte length
Post-pick views per pick	ToteWallRGB.jpg, OnArmLowRGB.jpg, FarTrayRGB.jpg (task page)	In the sample archive, FarTrayRGB.jpg is absent: all 5 picks carry ToteWallRGB.jpg and OnArmLowRGB.jpg only. Whether the full archive includes the far-tray view was not verified.
Reference images per product	"Up to 6 images (1.jpg-6.jpg)"	Confirmed as a range, not a constant: in the sample some products have all six, others fewer.

The FarTrayRGB.jpg gap matters if you plan to train on three post-pick views. Check for the file before you depend on it, and treat the per-pick view set as variable.

Verifying what you downloaded

The archive here is byte-identical to Amazon's release object, so the exact byte length is a usable integrity check:

hf download amazon/armbench-object-identification \
  archive/armbench-object-id-0.1.tar.gz \
  --repo-type dataset --local-dir ./armbench-object-identification

stat -c %s ./armbench-object-identification/archive/armbench-object-id-0.1.tar.gz
# expected: 109039658872

No SHA-256 is quoted in this card, deliberately. Amazon published a checksum sidecar alongside some ARMBench release objects but not, as far as could be verified at the time of writing, alongside this one: no .sha256 file accompanies this subset's release object, and a HeadObject on the candidate sidecar key identification/armbench-object-id-0.1.tar.gz.sha256 in Amazon's release bucket returned HTTP 404 on 2026-10-28. Rather than print a hash this redistribution computed itself and imply Amazon published it, this card gives the byte length. If you need a strong hash, compute one and compare it with another downloader.

Format check, 3 MB instead of 101.551 GiB - only if the Files and versions tab lists the sample (it is a separate object in Amazon's release and is not guaranteed to be mirrored here):

hf download amazon/armbench-object-identification \
  archive/armbench-object-id-tiny.tar.gz \
  --repo-type dataset --local-dir ./armbench-object-identification
mkdir -p ./oid-sample && tar -xzf \
  ./armbench-object-identification/archive/armbench-object-id-tiny.tar.gz -C ./oid-sample

Dataset Creation

The data was collected in Amazon warehouses from a robotic manipulator performing object singulation from containers with heterogeneous contents, as described in the ARMBench paper:

This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. [...] We present a large-scale dataset collected in an Amazon warehouse using a robotic manipulator performing object singulation from containers with heterogeneous contents. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick-and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com

The polygon segments and ground-truth product ids were produced by human annotators. Amazon's website does not publish the annotation protocol, the annotator population, or inter-annotator agreement, and this redistribution does not add them.

Release history, verbatim from the ARMBench website

June, 2026: New benchmark dataset released for Blade Insertion Affordance Prediction, with ~29,000 cycles of RGBD images and 3D motion trajectories.

August, 2025: New benchmark dataset released for Stow Success Prediction, adding a fourth key challenge to ARMBench.

March, 2023: Benchmark datasets publicly released for 3 key perception challenges in robotic manipulation: (a) Object segmentation, (b) Object identification, and (c) Defect detection.

January, 2023: The ARMBench research paper has been accepted for presentation at ICRA, 2023.

Considerations for Using the Data

Intended use. Research and benchmarking of open-set object identification - image retrieval, classification and uncertainty estimation - for robotic manipulation.

Open set, not closed set. The point of this subset is that the object catalogue is large and incomplete. A model that assumes every query maps to a known gallery entry is solving a different problem. The home page explicitly asks for "uncertainty estimation"; report a reject/abstain behaviour, not only top-1 accuracy.

Scope limits. The images come from one class of Amazon Robotics workcell in Amazon fulfillment centres, under that facility's lighting, camera placement and object mix. Product ids are Amazon catalogue identifiers and the catalogue drifts over time. Models trained here should not be assumed to transfer to other workcells, camera geometries or inventory distributions without re-evaluation.

Segment quality differs by view. As measured above, the in-container polygon is dense and the post-pick polygons are coarse. A model fed raw polygon crops will see systematically different mask fidelity pre-pick and post-pick.

Personal and sensitive information. The images show products being handled inside the robotic workcell. Amazon's website does not publish a de-identification statement for this subset, and this redistribution does not add one. If you find content that should not be public, please contact armbench-support@amazon.com.

Files in this repository (generated from the uploaded tree)

Path	Bytes
License-CC-BY.txt	18,657
archive/armbench-object-id-tiny.tar.gz	3,126,329

The large release archive is listed in the sections above; the files here are the small companion objects (Amazon's published checksum sidecars, the tiny sample archives, the official split lists and the released baseline checkpoints), all unmodified.

Licence and attribution

Quoted verbatim from the ARMBench website's Credits section:

These datasets are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). Use the following citations if you use these datasets for publication.

Licence	CC BY 4.0 (cc-by-4.0)
Original data credit	Amazon.com, Inc. - ARMBench, http://armbench.com
Modifications to the media	None. archive/armbench-object-id-0.1.tar.gz is byte-for-byte identical to Amazon's release object, at 109,039,658,872 bytes. Any sample archive mirrored alongside it is likewise unmodified.
Additions	None. This repository adds no derived tables, no Parquet and no baseline checkpoint; it is Amazon's release archive and this card.
Further restrictions	None are applied. This redistribution adds no terms beyond CC BY 4.0.

Attribution is a binding condition of CC BY 4.0. If you use this data, cite the paper below.

The ARMBench suite on the Hub

ARMBench is distributed as six downloads, each published as its own dataset under the amazon organization so that you can take only the subset you need. Every one carries the armbench tag, so datasets?other=armbench lists the whole family, and they are grouped in the ARMBench collection on the organization page.

Dataset	Program	Task	Download size
amazon/armbench-segmentation	Sparrow	Object segmentation in clutter	18.5 GiB
amazon/armbench-object-identification (this dataset)	Sparrow	Open-set object identification	101.6 GiB
amazon/armbench-defect-image	Sparrow	Defect detection, images	65.7 GiB
amazon/armbench-defect-video	Sparrow	Defect detection, videos	254.0 GiB
amazon/armbench-stow-success-prediction	Vulcan Stow	Stow success prediction	37.4 GiB
amazon/armbench-blade-insertion-affordance	Vulcan Stow	Blade insertion affordance	555.7 GiB

The Sparrow subsets are cited with mitash2023armbench (below); the two Vulcan Stow subsets are cited with hudson2025stow - see each dataset's own card for its correct citation.

Citation

@article{mitash2023armbench,
  title     = {ARMBench: An object-centric benchmark dataset for robotic manipulation},
  author    = {Mitash, Chaitanya and Wang, Fan and Lu, Shiyang and Terhuja, Vikedo
               and Garaas, Tyler and Polido, Felipe and Nambi, Manikantan},
  journal   = {arXiv preprint arXiv:2303.16382},
  year      = {2023}
}

This is Amazon's published BibTeX entry with one character removed: the version on armbench.com has a stray comma after Vikedo, which makes BibTeX parse the name under its three-part last, jr, first rule and drop Vikedo Terhuja's given name from the rendered reference. The seven authors and their order are unchanged and match the arXiv metadata for 2303.16382. No @inproceedings ICRA entry is given because arXiv carries no journal reference for this paper and the proceedings citation could not be verified.

Contact

Quoted verbatim from the ARMBench website:

For questions or to provide feedback, please reach out to us at armbench-support@amazon.com

(c) Amazon.com