amazon/armbench-defect-video

ARMBench - Defect Detection, Video Subset

This repository is the official Amazon redistribution, on the Hugging Face Hub, of the video defect detection subset of ARMBench. The media payload is Amazon's original release archive, byte-for-byte, with its published SHA-256 intact; the small label, annotation and manifest files alongside it are derived artifacts added so that the labels can be read without downloading 254 GiB.

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.

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.

The defect detection task, verbatim

The defect detection task is to identify if a robotic manipulation activity resulted in a defect. Two types of robot-induced defects are included in the dataset: 1. multi-pick, and 2. package-defect. Multi-pick is used to describe activities where multiple objects were picked and transferred from the source container to the destination container. Package-defect is used to describe activities where the object packaging opened and/or the object separated into multiple parts (deconstruction). Two subclasses, open and deconstruction, are defined for package-defect.

This subset, verbatim

Video defect detection (255G): This dataset comprises 4,075 videos of objects with defects. Multi-picks are not observable in videos and are excluded from this dataset. At the same time, open and deconstruction defects are observable in videos and are annotated. 100,000 videos of activities that did not result in a defect are available in the dataset.

Train-Test-Split: List of training and test cases.

Two editorial notes on the quotations above, so that the differences from the live page are auditable and not mistaken for transcription errors. First, parentheses and italics were added to ...separated into multiple parts (*deconstruction*); the live page italicises the term but omits the parentheses. Second, the live page's sentence "For image defection detection, ..." belongs to the image subset and is not quoted here. Nothing else was changed, added or reordered.

Dataset Structure

What this repository contains

Path	Bytes	Description
archive/armbench-defects-video-0.1.tar.gz	272,781,053,674	Amazon's original release archive, unmodified
archive/armbench-defects-video-0.1.tar.gz.sha256	102	Amazon's published checksum sidecar, unmodified
archive/armbench-defects-tiny.tar.gz	69,609,824	Amazon's small defect-detection sample archive (covers both the image and video subsets), unmodified
labels/train-00000-of-00001.parquet	-	8,757 rows, derived (see schema below)
labels/test-00000-of-00001.parquet	-	11,251 rows, derived
annotations/train.csv	192,654	Amazon's official train split list, unmodified
annotations/test.csv	247,522	Amazon's official test split list, unmodified
annotations/video_annotations_104075.jsonl	8,649,828	All 104,075 per-video annotation JSONs, one per line, derived
annotations/video_defect_labels_ALL_104075.csv	5,068,525	Flattened clip-level labels for all 104,075 videos, derived
annotations/archive_listing_verbose.txt	19,774,629	tar -tvzf listing of every archive member with its size, 208,156 lines, derived
baseline/defect_detection_videos.ckpt	436,120,692	Amazon's released baseline checkpoint, unmodified
License-CC-BY.txt	-	The licence file shipped at the root of Amazon's archive, unmodified

Archive contents

The archive holds, at its root:

• videos/<pick_id>.mp4 - 104,075 videos, 1280x560
• annotations/<pick_id>.json - 104,075 annotation files, one per video, 1:1 with the videos
• train.csv, test.csv - the official split lists
• License-CC-BY.txt

The annotation files sit at the end of the gzip stream. A partial download, or tar tzf ... | head, shows only videos/ and the archive will appear to be unlabelled. It is not. The gzip stream is not seekable, so any full listing or extraction is a single sequential pass (roughly 25 minutes on a modern machine). If you only need labels, use labels/ or annotations/ in this repository and never touch the archive.

labels configuration (the default, and what the viewer shows)

One row per pick in the official train/test split. 20,008 rows total.

Column	Type	Description
pick_id	string	Pick identifier; the archive member is videos/<pick_id>.mp4
split	string	train or test, from Amazon's own train.csv / test.csv
rel_path	string	Path of the video inside the archive
label	string	Clip-level class: nominal, open or deconstruction
y_open	int8	Multi-hot target channel 0
y_deconstruction	int8	Multi-hot target channel 1
ev_open	int8	1 if any frame-level event in this clip is an open event
ev_deconstruction	int8	1 if any frame-level event in this clip is a deconstruction event
sublabel	string	Item type for defect clips: book, box, bag, other; empty for nominal
defect_onset_frames	string	Semicolon-separated frame indices of the events matching the clip label; empty for nominal
onset_frames_all_events	string	Semicolon-separated frame indices of all annotated events, including events whose class differs from the clip label

The raw per-video annotation schema, as shipped inside the archive and reproduced line-by-line in annotations/video_annotations_104075.jsonl, is {"id", "label", "sublabel", "annotations": [{"frame_idx", "label"}]}.

Splits

Split	Videos	nominal	deconstruction	open	Defect rate
train	8,757	5,838	1,520	1,399	33.3%
test	11,251	10,000	645	606	11.1%
total	20,008	15,838	2,165	2,005	20.8%

The test split is deliberately larger than the train split. Train is defect-enriched (33.3% defects) to make learning tractable; test approximates the operational rate (11.1%). This is by design. Do not rebalance it if you are reporting benchmark numbers.

The remaining 84,067 videos in the archive (205.485 GiB, 80.60% of the archive's video bytes) are not part of the official split. They carry annotations and can be used for pre-training or self-supervision, but they are not part of the benchmark. Clip-level labels for all 104,075 videos, in and out of split, are in annotations/video_defect_labels_ALL_104075.csv.

Sizes of the useful subsets, measured from the archive's own member table:

Tier	Videos	Bytes	Size
Whole archive	104,075	272,781,053,674	254.047 GiB
Official train + test	20,008	53,094,867,799	49.448 GiB
Official test only	11,251	29,747,682,304	27.705 GiB
Defect videos only	4,170	-	10.849 GiB
Labels and annotations only	-	~34,000,000	~33 MiB

The benchmark target

The official target is multi-hot over two independent sigmoid channels, not a 3-way softmax and not a binary defect/no-defect collapse:

Clip label	[y_open, y_deconstruction]
nominal	[0, 0]
open	[1, 0]
deconstruction	[0, 1]

Amazon's released baseline, baseline/defect_detection_videos.ckpt, has a (2, 768) classification head trained with binary cross-entropy, which is the reason to keep two channels rather than three classes.

At the clip level the pair [1, 1] does not occur in this release, so a 3-class model would be numerically equivalent on these labels. At the frame-event level the two classes do co-occur: 946 of the 20,008 clips contain both an open event and a deconstruction event even though their clip label names only one. Models that consume frame events need the two channels.

Known data quirks

These are measured properties of the release, documented so you do not have to rediscover them.

1. Frame-event label strings carry trailing spaces. The event vocabulary includes "Open " and "Deconstruction " with a trailing space, as well as "No Damage" and null. Strip before comparing.
2. Some frame_idx values exceed the true frame count of the corresponding mp4. Clamp to the last frame; do not subtract an offset.
3. defect_onset_frames is a strict subset of onset_frames_all_events in 343 of 20,008 rows (1.7%). If you need every annotated event, read onset_frames_all_events.
4. 15,838 rows have an empty defect_onset_frames. These are exactly the nominal clips.
5. gzip buys almost nothing here - 0.416% on already-encoded mp4. The archive is gzipped because Amazon released it that way, not because it compresses.
6. annotations/archive_listing_verbose.txt is Amazon's member listing with sizes. In some earlier working copies this file was misleadingly named partial_tar_listing.txt; it is the complete 208,156-line verbose listing, not a partial one.

Published figures versus measured figures

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

Quantity	Amazon's published figure	Measured from this release
Defect videos	4,075 (task page), 4,000+ (home page)	4,170 (2,165 deconstruction + 2,005 open), over all 104,075 annotations
Non-defect videos	100,000 (task page), 100,000+ (home page)	99,905 nominal (95.99% of 104,075)
Download size	255G (task page), 254 GB (release email)	272,781,053,674 bytes = 254.047 GiB = 272.78 GB

The 4,075 / 4,170 gap has been raised with the ARMBench team. Until it is resolved, treat 4,170 as the count you will actually find in the files and 4,075 as the published figure.

Verifying what you downloaded

The archive in this repository is byte-identical to Amazon's release object, so Amazon's own published checksum verifies it:

hf download amazon/armbench-defect-video \
  archive/armbench-defects-video-0.1.tar.gz \
  archive/armbench-defects-video-0.1.tar.gz.sha256 \
  --repo-type dataset --local-dir ./armbench-defect-video

cd ./armbench-defect-video/archive
sha256sum -c armbench-defects-video-0.1.tar.gz.sha256
# expected: a0dccbc3e92f7587bf9bd8fa5f57facb7ce7e04b4303680847dbdbe62aaa9905

Labels only, no archive:

from datasets import load_dataset
ds = load_dataset("amazon/armbench-defect-video")   # the `labels` config, 20,008 rows
print(ds["train"][0])

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

Clip-level and frame-level defect annotations 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 video-based defect detection for robotic manipulation.

Scope limits. The videos come from one class of Amazon Robotics workcell in Amazon fulfillment centres, under that facility's lighting, camera placement and object mix. Models trained here should not be assumed to transfer to other workcells, camera geometries or inventory distributions without re-evaluation. Multi-pick defects are excluded from this subset because they are not observable in the video view; a model trained here cannot detect them.

Class imbalance. Defects are rare by design in the operation being measured (4.01% of all 104,075 videos). Accuracy is a poor metric; report per-class precision/recall or average precision, and report them on the official test split, whose 11.1% defect rate is the realistic one.

Personal and sensitive information. The videos show items 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.

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-defects-video-0.1.tar.gz is byte-for-byte identical to Amazon's release object and verifies against Amazon's published SHA-256.
Additions	The labels/ Parquet tables and the derived files in annotations/ marked "derived" in the file table above were produced from the archive's own annotation files for this redistribution. One file was renamed: partial_tar_listing.txt to annotations/archive_listing_verbose.txt.
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	Sparrow	Open-set object identification	101.6 GiB
amazon/armbench-defect-image	Sparrow	Defect detection, images	65.7 GiB
amazon/armbench-defect-video (this dataset)	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