Datasets:
Tasks:
Video Classification
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
armbench
amazon-robotics
robotic-manipulation
defect-detection
video-classification
warehouse-automation
License:
| license: cc-by-4.0 | |
| language: | |
| - en | |
| pretty_name: ARMBench Defect Detection - Video Subset | |
| tags: | |
| - armbench | |
| - amazon-robotics | |
| - robotic-manipulation | |
| - defect-detection | |
| - video-classification | |
| - warehouse-automation | |
| task_categories: | |
| - video-classification | |
| size_categories: | |
| - 100K<n<1M | |
| annotations_creators: | |
| - expert-generated | |
| source_datasets: | |
| - original | |
| configs: | |
| - config_name: labels | |
| default: true | |
| data_files: | |
| - split: train | |
| path: labels/train-*.parquet | |
| - split: test | |
| path: labels/test-*.parquet | |
| # 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](https://arxiv.org/abs/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`](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 | |
| > **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: | |
| ```bash | |
| 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: | |
| ```python | |
| 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](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-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`](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) | Sparrow | Defect detection, images | 65.7 GiB | | |
| | [`amazon/armbench-defect-video`](https://huggingface.co/datasets/amazon/armbench-defect-video) **(this dataset)** | 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 |