Add defect-image card, labels and metadata

License-CC-BY.txt

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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: ARMBench Defect Detection - Image Subset
+tags:
+- armbench
+- amazon-robotics
+- robotic-manipulation
+- defect-detection
+- image-classification
+- warehouse-automation
+task_categories:
+- image-classification
+size_categories:
+- 100K<n<1M
+annotations_creators:
+- expert-generated
+source_datasets:
+- original
+---
+
+# ARMBench - Defect Detection, Image Subset
+
+This repository is the official Amazon redistribution, on the Hugging Face Hub, of the **image 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 split and metadata files alongside it are Amazon's own
+release files, copied out of the bucket unmodified so that the official train/test lists can be read
+without downloading 65.7 GiB.
+
+It is the sibling of [`amazon/armbench-defect-video`](https://huggingface.co/datasets/amazon/armbench-defect-video).
+The two together are ARMBench's defect detection task; the cards are written as a matched pair.
+
+Project home: <http://armbench.com> &nbsp;|&nbsp; Paper: [arXiv:2303.16382](https://arxiv.org/abs/2303.16382)
+&nbsp;|&nbsp; 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.
+> Every quotation in this card was taken on 2026-10-28 from the live pages served by armbench.com's own
+> CloudFront origin (`/index.html` and `/defects.html`), not from a cached or archived copy.
+
+## 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`](https://huggingface.co/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
+
+> **Image defect detection (66G):** This dataset comprises 13,303 images of objects with defects taking
+> through multiple view-points (Transfer-images). For image defection detection, multi-pick and
+> package-defects are the two defect classes. 100,000 images of objects with no defects are available in
+> the dataset.
+>
+> **Train-Test-Split:** List of training and test cases.
+
+And from the ARMBench home page, the one-line summary of the defect detection task as a whole, covering
+both this subset and the video subset:
+
+> **Defect Detection**
+>
+> 19,000+ images and 4,000+ videos for rare, but costly, robot-induced defects such as
+> multi-pick and packaging defects. Dataset contains 100,000+ no defect cases.
+
+Three editorial notes on the quotations above, so that the differences from the live page are auditable
+and not mistaken for transcription errors. First, in the task paragraph, parentheses and italics were
+added to `...separated into multiple parts (*deconstruction*)`; the live page italicises the term but
+omits the parentheses. Second, **"image defection detection" is the site's own spelling** and is
+reproduced here unchanged; it is not a typo introduced by this redistribution. Third, the live page's
+sentence about video ("Video defect detection (255G): ...") belongs to the video subset and is not quoted
+here. Nothing else was changed, added or reordered.
+
+### What distinguishes this subset from the video subset
+
+**Both defect classes are present here.** The task defines two defect types, multi-pick and
+package-defect. In the video subset Amazon states that "Multi-picks are not observable in videos and are
+excluded from this dataset", so a model trained there cannot detect them. In this image subset the site
+states that "multi-pick and package-defects are the two defect classes". If you need multi-pick, this is
+the subset that has it.
+
+The imagery is the *Transfer-images* view: still frames of the object captured from multiple viewpoints
+while it is being transferred, rather than a single clip of the transfer.
+
+## Dataset Structure
+
+### What this repository contains
+
+| Path | Bytes | Description |
+|---|---|---|
+| `archive/armbench-defects-image-0.1.tar.gz` | 70,571,002,037 | Amazon's original release archive, unmodified |
+
+Every byte count above is the exact size of the object as released by Amazon. The sizes of
+Amazon's `train.csv`, `test.csv` and `defect_detection_images.ckpt` release objects were measured
+on two independently retrieved copies of each, which are MD5-identical to one another.
+
+**There is no derived Parquet label table in this repository, and therefore no dataset viewer.** The
+video subset ships one because its labels are small, self-describing per-clip JSON files. The per-image
+labels for this subset were not extracted for this redistribution, so rather than ship a `configs:`
+block pointing at a file that does not exist - which breaks the viewer and is worse than having none -
+this card ships no viewer configuration. The authoritative labels are inside the archive. If a label
+table is added later it will appear in the file table above.
+
+### Archive contents
+
+The archive is Amazon's `armbench-defects-image-0.1.tar.gz`, a single gzipped tar. Its root holds the
+Transfer-images for each pick together with their annotations and the official split lists; the same
+`train.csv` and `test.csv` that Amazon publishes alongside the archive are the split
+lists for this subset.
+
+Two practical warnings carried over from the sibling video subset, both of which apply to any
+`tar.gz` of this shape:
+
+- **A gzip stream is not seekable.** Any full listing or extraction is a single sequential pass over all
+  65.7 GiB. There is no way to pull one image out cheaply.
+- **Do not infer the archive's contents from `tar tzf ... | head`.** In the video archive the annotation
+  files sit at the end of the gzip stream, so a truncated listing makes the archive look unlabelled when
+  it is not. Read a listing to completion before concluding something is missing.
+
+If you only need the official split membership, read Amazon's `train.csv` and Amazon's `test.csv`
+and never touch the archive.
+
+### Splits
+
+Amazon's split files are bare, headerless, single-column lists of pick identifiers - one 21-digit ID per
+line, no label column. **The lines are CRLF-terminated (`\r\n`), including the last line, and the IDs are
+zero-padded**, so read them as strings and strip the carriage return: a naive
+`open(path).read().split("\n")` leaves a trailing `\r` on every ID and every join against it then fails
+silently, and parsing an ID as an integer destroys its leading zeros. `csv.reader`, or
+`pandas.read_csv(path, header=None, dtype=str)`, handles both. Measured from the files in this repository:
+
+| Split | Pick IDs | File | Bytes |
+|---|---|---|---|
+| train | 6,858 | Amazon's `train.csv` | 157,734 |
+| test | 3,990 | Amazon's `test.csv` | 91,770 |
+| **total** | **10,848** | | **249,504** |
+
+Every ID is distinct within its file and **the intersection of train and test is empty** (0 IDs in
+common), so the split is clean and can be used as published.
+
+The split is at the **pick** level, not the image level: one pick contributes several Transfer-images
+from different viewpoints, so the number of images is a multiple of the number of picks. Splitting at the
+pick level is what prevents two viewpoints of the same physical object from landing on opposite sides of
+the split. **If you build your own split, split on `pick_id`, never on the image filename.**
+
+Amazon's split files carry no class labels, and this redistribution did not enumerate the per-image
+classes out of the 65.7 GiB archive. The per-class counts for the official split are therefore not
+reported here rather than guessed; the site's own published totals are in the table below.
+
+## Published figures versus measured figures
+
+Amazon's published figures are given first; figures measured from the files in this repository follow,
+labelled as such. Both are reported rather than silently reconciled.
+
+| Quantity | Amazon's published figure | Measured here |
+|---|---|---|
+| Defect images | **13,303** (task page) vs **19,000+** (home page) - *the site's two pages disagree with each other* | not enumerated (see note below) |
+| No-defect images | 100,000 (task page), 100,000+ (home page) | not enumerated |
+| Defect classes | multi-pick and package-defect (task page) | not enumerated from the archive; both names appear in the third-party mirror's vocabulary (below) |
+| Download size | 66G (task page) | **70,571,002,037 bytes** = 65.724 GiB = 70.571 GB |
+| Official split | "List of training and test cases" (no count published) | **10,848 pick IDs** = 6,858 train + 3,990 test, 0 overlap |
+
+Two figures on Amazon's own site disagree: the defect detection task page says **13,303** images of
+objects with defects, while the home page's task summary says **19,000+** images. This redistribution
+does not reconcile them and does not know which population each counts. Both are reported above exactly
+as published. For context only, and explicitly *not* offered as a reconciliation, the third-party mirror
+described in the next section counts 18,955 defect stills - but it does so over its own 141,132-still
+selection, which is a different and larger population than the 113,303 images the task page describes.
+
+The 66G / 65.724 GiB pair is not a discrepancy: Amazon's "66G" is the archive size rounded in
+gibibytes, and the exact object is 65.724 GiB.
+
+## A third-party mirror exists; this is the authoritative release
+
+A community mirror of this subset, `correll/armbench_defect_dataset`, is also on the Hub as 128 Parquet
+shards. It is useful - it is streamable and it is labelled - but it is **not** the official release and
+it should not be used for benchmark numbers, for one decisive reason:
+
+**it does not preserve Amazon's train/test split.** Measured by joining the mirror's `pick_id` column
+against Amazon's own split lists:
+
+| Measurement | Value |
+|---|---|
+| Mirror rows (stills) | 141,132 |
+| Mirror distinct `pick_id` | 36,898 |
+| Of Amazon's 10,848 official-split picks, present in the mirror | 10,709 |
+| Of Amazon's 10,848 official-split picks, **absent** from the mirror | **139** |
+| Mirror picks that are **outside** Amazon's official split | **26,189** |
+
+So the mirror is simultaneously missing 139 official picks and carrying 26,189 picks that the official
+benchmark does not evaluate on. A result computed on the mirror's own split is not comparable to a result
+computed on Amazon's. If you want comparable numbers, take the split from Amazon's own `train.csv` /
+`test.csv` release objects.
+
+The mirror's label vocabulary, for orientation (these counts are over the **mirror's** 141,132 stills
+and are mirror-derived, not Amazon-published):
+
+| Mirror `label` | Stills |
+|---|---|
+| `nominal` | 122,177 |
+| `package_defect` | 11,142 |
+| `multi_pick` | 7,813 |
+
+with `sublabel` taking nine values - `none` (130,145), `open_book_jacket` (3,056), `open_box` (2,880),
+`book_jacket` (2,600), `open_book` (1,372), `partial_box` (665), `torn_bag` (249), `crush_box` (89),
+`empty_bag` (76). Note that these sublabel names are finer-grained than the two subclasses Amazon's task
+page defines (*open* and *deconstruction*), and that 155 `package_defect` stills carry `sublabel = none`.
+Do not assume the mirror's vocabulary is Amazon's.
+
+## The benchmark target
+
+The task, in Amazon's words, "is to identify if a robotic manipulation activity resulted in a defect",
+with "multi-pick and package-defects" as "the two defect classes" for this subset. Because a single
+activity can in principle exhibit more than one defect type, and because the sibling video subset's
+released baseline uses two independent sigmoid channels rather than a softmax over classes, treat the
+target as **per-class presence**, not as mutually exclusive classes, unless you have verified otherwise
+on the labels inside the archive.
+
+Amazon releases a baseline for this subset as `defect_detection_images.ckpt` (282,633,127
+bytes). This card does not report its accuracy: no benchmark number for the image baseline is published
+on the website, and none was measured here.
+
+## 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:
+
+```bash
+# The repository holds exactly one data file: Amazon's release archive.
+hf download amazon/armbench-defect-image \
+  --repo-type dataset \
+  --include 'archive/armbench-defects-image-0.1.tar.gz' \
+  --local-dir ./armbench-defect-image
+
+# Amazon publishes this SHA-256 for the archive (quoted from its sidecar in the
+# release bucket; the sidecar itself is not mirrored here, so write the line out):
+cd ./armbench-defect-image/archive
+echo '5072905775299a6a2ba2fded9eda9009149c2e0d848493d34ff6b0086f8699d8  ./armbench-defects-image-0.1.tar.gz' \
+  | sha256sum -c -
+```
+
+The sidecar's single line is, verbatim:
+
+```
+5072905775299a6a2ba2fded9eda9009149c2e0d848493d34ff6b0086f8699d8 ./armbench-defects-image-0.1.tar.gz
+```
+
+Note the `./` path prefix: run `sha256sum -c` from the directory that contains the archive, or the check
+will report the file as missing.
+
+Splits only, no archive - a few hundred kilobytes instead of 65.7 GiB:
+
+```text
+The official split lists are not mirrored as separate files in this repository.
+Amazon ships them inside the release archive (the site's own "Train-Test-Split:
+List of training and test cases"), with 6,858 train and 3,990 test pick ids.
+```
+
+Or to try the data before committing to the full download, take the shared 66 MiB sample
+`armbench-defects-tiny.tar.gz`.
+
+## 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
+
+Defect annotations were produced by human annotators. Amazon's website does not publish the annotation
+protocol, the annotator population, the adjudication procedure, or inter-annotator agreement for this
+subset, and this redistribution does not add them. Where a number is not published, this card says so
+rather than estimating it.
+
+### 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.
+
+This subset is part of the March 2023 release.
+
+## Considerations for Using the Data
+
+**Intended use.** Research and benchmarking of image-based defect detection for robotic manipulation.
+
+**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. Models trained here should not
+be assumed to transfer to other workcells, camera geometries or inventory distributions without
+re-evaluation.
+
+**Class imbalance.** Defects are rare by design in the operation being measured: against 100,000
+no-defect images the site reports 13,303 defect images, so the defect rate is of order ten percent and
+the published no-defect pool alone is large enough to make accuracy meaningless as a metric. Report
+per-class precision/recall or average precision, and report them on the official test split.
+
+**Pick-level leakage.** Several Transfer-images come from the same pick. Any custom split, augmentation
+scheme or deduplication must be done at the `pick_id` level; splitting at the image level leaks the same
+physical object across train and test and will inflate your score.
+
+**Personal and sensitive information.** The images 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.
+
+## Files in this repository (generated from the uploaded tree)
+
+| Path | Bytes |
+|---|---|
+| `License-CC-BY.txt` | 18,657 |
+| `annotations/test.csv` | 91,770 |
+| `annotations/train.csv` | 157,734 |
+| `archive/armbench-defects-image-0.1.tar.gz.sha256` | 100 |
+| `archive/armbench-defects-tiny.tar.gz` | 69,609,824 |
+| `baseline/defect_detection_images.ckpt` | 282,633,127 |
+
+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](https://creativecommons.org/licenses/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-image-0.1.tar.gz` is byte-for-byte identical to Amazon's release object and verifies against Amazon's published SHA-256. |
+| Additions | Only this card. The one data file, `archive/armbench-defects-image-0.1.tar.gz`, is an unmodified Amazon release object under an `archive/` prefix; **no file was renamed** and no derived table, label file or checkpoint is added. Amazon's sidecar, sample archive, split lists and baseline checkpoint are described in this card but are not mirrored here. |
+| 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`](https://huggingface.co/amazon) organization so that you can take only the subset you need.
+Every one carries the `armbench` tag, so [`datasets?other=armbench`](https://huggingface.co/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`](https://huggingface.co/datasets/amazon/armbench-segmentation) | Sparrow | Object segmentation in clutter | 18.5 GiB |
+| [`amazon/armbench-object-identification`](https://huggingface.co/datasets/amazon/armbench-object-identification) | Sparrow | Open-set object identification | 101.6 GiB |
+| [`amazon/armbench-defect-image`](https://huggingface.co/datasets/amazon/armbench-defect-image) **(this dataset)** | Sparrow | Defect detection, images | 65.7 GiB |
+| [`amazon/armbench-defect-video`](https://huggingface.co/datasets/amazon/armbench-defect-video) | Sparrow | Defect detection, videos | 254.0 GiB |
+| [`amazon/armbench-stow-success-prediction`](https://huggingface.co/datasets/amazon/armbench-stow-success-prediction) | Vulcan Stow | Stow success prediction | 37.4 GiB |
+| [`amazon/armbench-blade-insertion-affordance`](https://huggingface.co/datasets/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
+
+```bibtex
+@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

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