# Object Detection Datasets: AI Datasets
Source: https://savrn.com/datasets/tasks/object-detection
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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SAVRN Model Hub · Datasets by Task

# Object Detection Datasets

16 open-weight object detection datasets in the SAVRN Model Hub, with Fire Viewer, AgML and Oh Yeongkwon publishing the most.

16Datasets

12Publishers

6Licenses

## Most Downloaded

| Dataset | Publisher | License | Monthly downloads |
| --- | --- | --- | --- |
| [cati-singapore-dataset](https://savrn.com/datasets/cati-singapore-dataset) | Suhas Reddy | mit | 3.3k |
| [fire-smoke-detection-corpus-v2](https://savrn.com/datasets/fire-smoke-detection-corpus-v2) | Fire Viewer | other | 1.7k |
| [M6Doc](https://savrn.com/datasets/m6doc) | Zhiyuan Zhao | Not stated | 1.1k |
| [fire-smoke-detection-corpus-v1](https://savrn.com/datasets/fire-smoke-detection-corpus-v1) | Fire Viewer | other | 997 |
| [flame_smoke_detection_box_not_precise](https://savrn.com/datasets/flame-smoke-detection-box-not-precise) | Fire Viewer | other | 536 |
| [fire_and_smoke_detection_very_hard_negative](https://savrn.com/datasets/fire-and-smoke-detection-very-hard-negative) | Fire Viewer | other | 244 |
| [polyp-regime-bench-data](https://savrn.com/datasets/polyp-regime-bench-data) | AI-for-Medicine-and-Health | Not stated | 99 |
| [tracking_anything_datasets](https://savrn.com/datasets/tracking-anything-datasets) | Wen | other | 77 |
| [fixhome-devices](https://savrn.com/datasets/fixhome-devices) | Pham Duc Toan | other | 65 |
| [raccoon-corrected](https://savrn.com/datasets/raccoon-corrected) | KeenForgeAI | mit | 21 |

## Licenses

| License | Datasets | Commercial use |
| --- | --- | --- |
| other | 6 | Read the license |
| mit | 4 | Yes |
| not stated | 2 | Not stated |
| cc-by-4.0 | 2 | Yes |
| gpl | 1 | Read the license |
| cc-by-nc-4.0 | 1 | Not without separate permission |

## Who Publishes Them

| Publisher | Datasets |
| --- | --- |
| [Fire Viewer](https://savrn.com/model-publishers/fireviewer) | 4 |
| [AgML](https://savrn.com/model-publishers/project-agml) | 2 |
| [Oh Yeongkwon](https://savrn.com/model-publishers/yeongkwon) | 1 |
| [Dylan Tirandaz](https://savrn.com/model-publishers/tirandazdylan) | 1 |
| [Suhas Reddy](https://savrn.com/model-publishers/suhxsreddy) | 1 |
| [Pham Duc Toan](https://savrn.com/model-publishers/phamductoan3883) | 1 |

## All 16 Datasets

Dataset · Object detection

### [cati-singapore-dataset](https://savrn.com/datasets/cati-singapore-dataset)

[Suhas Reddy](https://savrn.com/model-publishers/suhxsreddy)

Real-time vehicle detection data collected from Singapore's 90 LTA traffic cameras using CATI (Context-Aware Traffic Intelligence) — a novel FiLM-conditioned YOLOv11 detector that adapts to environmental conditions in real time. This dataset contains per-camera vehicle detection results collected continuously from Singapore's Land Transport Authority (LTA) expressway camera network. Each record captures a full detection sweep of a single camera including vehicle counts, class breakdown, directional split, and environmental context. Detections are produced by CATI — a novel architecture that injects FiLM (Feature-wise Linear Modulation) layers into YOLOv11s, conditioning the backbone on…

Publicly accessible mit 10K<n<100K

[View dataset](https://savrn.com/datasets/cati-singapore-dataset)

Dataset · Object detection

### [fire-smoke-detection-corpus-v1](https://savrn.com/datasets/fire-smoke-detection-corpus-v1)

[Fire Viewer](https://savrn.com/model-publishers/fireviewer)

Versioned detector-training corpus. Current V1 repository contents: 87,568 rows, split 53,952 train / 12,787 validation / 20,829 test. The corpus stores source-specific provenance, hashes, grouping/de-duplication information, validation status and annotation metadata. It preserves the V1 training reference and provenance used by earlier strict FireViewer detector releases; each model receipt identifies the snapshot actually used. There is no single licence covering all source images. Keep repository-level license: other and preserve the source licence/attribution recorded for every sample. FireViewer publication must not overwrite source obligations. This corpus is a training/research…

Publicly accessible other 10K<n<100K

[View dataset](https://savrn.com/datasets/fire-smoke-detection-corpus-v1)

Dataset · Object detection

### [fire-smoke-detection-corpus-v2](https://savrn.com/datasets/fire-smoke-detection-corpus-v2)

[Fire Viewer](https://savrn.com/model-publishers/fireviewer)

auto-safe/ contains approved model proposals and unanimous negative GT. auto-bonzai/ contains Bonsai corrections confirmed with high confidence. manuelle/ contains annotations accepted or corrected by a human. global/ contains distant views with visible fire or smoke outside forest-wildfire scenes. wildfire/ contains distant views with visible forest wildfire. Flame boxes stay small; a distant smoke plume may cover a large area. gold/ is the human calibration holdout; it is excluded from training. Some gold rows provide presence and origin points without boxes; use goldscope. Images and source licences are embedded or recorded per row; there is no blanket image licence. See audit/lots/ for…

Publicly accessible other

[View dataset](https://savrn.com/datasets/fire-smoke-detection-corpus-v2)

Dataset · Object detection

### [M6Doc](https://savrn.com/datasets/m6doc)

[Zhiyuan Zhao](https://savrn.com/model-publishers/juliozhao)

Mirror of the M6Doc (Multi-form, Multi-type, Multi-layout, Multi-language, Multi-annotation, Multi-task) document layout analysis benchmark used for DocLayout-YOLO downstream training experiments. - images/: ~19,000 document page images (.jpg + matching.npy descriptors). The first 10,000 files are stored flat; overflow files are sharded into images/shard10/, images/shard11/,... (9,000 files each) because HuggingFace repos allow at most 10,000 files per directory. - labels/: YOLO-format.txt label files (74 layout classes). - labels.cache (Ultralytics label cache) intentionally excluded. -.ipynbcheckpoints/ excluded. On 2026-09-26, 4,050 truncated JPEG files (missing EOI marker) in the source…

Publicly accessible 10K<n<100K

[View dataset](https://savrn.com/datasets/m6doc)

Dataset · Object detection

### [flame_smoke_detection_box_not_precise](https://savrn.com/datasets/flame-smoke-detection-box-not-precise)

[Fire Viewer](https://savrn.com/model-publishers/fireviewer)

A prepared two-class detection dataset with 38,965 unique native images and 50,591 bounding boxes. It combines the retained FireViewer detection source snapshot with the published negative corpus, with reproducible grouped splits and a COCO materializer. The legacy repository identifier is retained; this card describes the v2.5 release recorded in manifest.json. Train also includes 504 negative images from the detection source; the negative columns above identify only the two imported negative subsets. Native-negative difficulty is unrated; the JPEGs retain their historical candidate designation. COCO category IDs are 1: smokevisible and 2: flamevisible. Map them explicitly to internal…

Publicly accessible other 10K<n<100K

[View dataset](https://savrn.com/datasets/flame-smoke-detection-box-not-precise)

Dataset · Object detection

### [fire_and_smoke_detection_very_hard_negative](https://savrn.com/datasets/fire-and-smoke-detection-very-hard-negative)

[Fire Viewer](https://savrn.com/model-publishers/fireviewer)

A versioned negative-image resource for fire/smoke detector training and false-positive analysis. The current publication contains 12,653 native source-labelled negatives and 195 retained JPEG candidates, in two separate configurations. The repository name does not establish that every image is a very hard negative. The native subset has no individual visual review or difficulty rating. Existing source classification annotations were the admission basis and were not changed into fresh manual annotations. These identifiers are the source namespaces recorded in the retained provenance, not newly inferred labels or authorizations. See source records and the For the separate JPEG collection…

Publicly accessible other 10K<n<100K

[View dataset](https://savrn.com/datasets/fire-and-smoke-detection-very-hard-negative)

Dataset · Object detection

### [polyp-regime-bench-data](https://savrn.com/datasets/polyp-regime-bench-data)

[AI-for-Medicine-and-Health](https://savrn.com/model-publishers/ai-for-medicine-and-health)

This repository defines the exact image sets used in PolypRegimeBench, a controlled benchmark of 12 polyp detectors across 6 training regimes. It contains manifests only: file lists, split assignments, filtered IDs, augmentation recipes and checksums. It does not redistribute any third-party endoscopy images, masks or videos. White-light images only. Narrow-band images were excluded so that modality is not confounded with the other factors. Split seed 42, fixed for all runs. Test sets are almost entirely polyp-positive (0, 5 and 0 negative images respectively). The augmentations transform image and mask together: flips, affine (scale, translation, rotation, shear), blur, colour/contrast and…

Publicly accessible

[View dataset](https://savrn.com/datasets/polyp-regime-bench-data)

Dataset · Object detection

### [tracking_anything_datasets](https://savrn.com/datasets/tracking-anything-datasets)

[Wen](https://savrn.com/model-publishers/jeffry77)

Release: tracking-anything-native-corpora-20261004. Upload: IN PROGRESS — do not treat partial uploads as a complete dataset. Original native archive/annotation mirror, not converted conversations or model-generated labels. No model weights, V8 memory cache, credentials, cookies, or runtime logs are included. This is not a strictly train-only export. TNL2K/VastTrack standalone test archives are excluded; LaSOT and TNLLT originals contain both train and test. COCO train2014 includes images used by REFER training, validation and test expressions. Use the original published splits; never train indiscriminately on every archive member. The small official TNL2K four-video patch also contains one…

Publicly accessible other

[View dataset](https://savrn.com/datasets/tracking-anything-datasets)

Dataset · Object detection

### [fixhome-devices](https://savrn.com/datasets/fixhome-devices)

[Pham Duc Toan](https://savrn.com/model-publishers/phamductoan3883)

Object-detection dataset of 22 household appliances, built to train the detector of FixHome, a SEP490 capstone project at FPT University: a marketplace that connects customers with home-repair technicians and gives a preliminary, AI-suggested diagnosis from a photo. The detector (YOLO11s) names the appliance and draws its box; the box crops the photo for a vision-language model further down the pipeline. The dataset leans towards appliances as they look in Vietnamese homes. Public datasets mostly show Western hardware: US and EU wall sockets where Vietnam uses two-pin universal sockets, built-in ranges where Vietnam uses countertop gas burners, front-loading washers where Vietnam is largely…

Publicly accessible other 10K<n<100K

[View dataset](https://savrn.com/datasets/fixhome-devices)

Dataset · Object detection

### [raccoon-corrected](https://savrn.com/datasets/raccoon-corrected)

[KeenForgeAI](https://savrn.com/model-publishers/keenforgeai)

A fully human-reviewed, re-annotated version of the classic Raccoon object detection dataset. Every one of the 193 images was manually reviewed; loose, missing, and incorrect bounding boxes were corrected. Every image was compared with the original VOC annotations using greedy IoU matching (matched ≥ 0.5, unchanged ≥ 0.95): Key observations - The original boxes were often loose (e.g., raccoon-1: box (81,88)-(522,408) → tightened to (80,105)-(529,405), IoU 0.91). The average IoU of matched boxes is 0.810, i.e. the corrected boxes bound the raccoon noticeably more tightly. - Occluded / partially visible raccoons were kept with tight boxes; several missed instances were added. - 7 exact/near…

Publicly accessible mit n<1K

[View dataset](https://savrn.com/datasets/raccoon-corrected)

Dataset · Object detection

### [CCOD_cotton_flower_detection](https://savrn.com/datasets/ccod-cotton-flower-detection)

[AgML](https://savrn.com/model-publishers/project-agml)

CCOD (Crop Canopy Organ-level Object Detection) is a UAV dataset of small plant organs seen from above crop canopies: cotton flowers and cotton bolls in Xinjiang, potato flowers at Keshan Farm in Heilongjiang, and rice panicles in Jiangsu, China. Images were captured at flowering or maturity from 12 m with the RGB camera of a DJI Mavic 3 Multispectral drone, cropped into 640 x 640 tiles (512 x 512 for rice), and annotated with dense bounding boxes, using a SAHI-YOLOv10n model to propose boxes that annotators then corrected. The cropcanopy column records which subset each image comes from, and the split column keeps the authors' 7:2:1 train/val/test split. The dataset contains 3,986 images…

Publicly accessible gpl 1K<n<10K

[View dataset](https://savrn.com/datasets/ccod-cotton-flower-detection)

Dataset · Object detection

### [dental-panoramic-xray-yolo](https://savrn.com/datasets/dental-panoramic-xray-yolo)

[DAng](https://savrn.com/model-publishers/danglili)

Combined dataset for dental pathology detection on panoramic radiographs, in YOLO format. Built for training liodon-ai/dental-panoramic-detector. DENTEX provides: caries, periapical lesion, impacted tooth annotations on panoramic X-rays from 3 institutions. OralXrays-9 provides: decay (→ caries), apical periodontitis (→ periapicallesion), wisdom tooth (→ impactedtooth) annotations across a hospital-scale panoramic X-ray collection. Validation uses DENTEX val set only for consistent benchmarking. Then point your YOLO config data: to {path}/dental.yaml. If you use this dataset, please cite: This dataset combines and remaps annotations from the following sources — please also cite them

Publicly accessible cc-by-nc-4.0 10K<n<100K

[View dataset](https://savrn.com/datasets/dental-panoramic-xray-yolo)

Dataset · Object detection

### [open-radiograph-findings](https://savrn.com/datasets/open-radiograph-findings)

[Dylan Tirandaz](https://savrn.com/model-publishers/tirandazdylan)

Source data for the radiograph-dicom-ingest Verifiers environment. Each row is one radiograph with: - the image file exactly as the source distributes it (GRAZPEDWRI-DX 16-bit PNG, FracAtlas JPEG; bytes are not re-encoded), - the source annotation files for that image, verbatim (Pascal VOC XML, YOLO txt, Supervisely JSON, COCO entries), - normalized bounding boxes derived from the source YOLO labels. The dataset contains no DICOM files. It is a research and benchmarking resource and is not for clinical use. Both sources are published under CC BY 4.0, and this subset is released under the same license. Changes from the sources: a subset of images was selected, the files were repackaged into…

Publicly accessible cc-by-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/open-radiograph-findings)

Dataset · Object detection

### [pcb-defect-coco](https://savrn.com/datasets/pcb-defect-coco)

[Oh Yeongkwon](https://savrn.com/model-publishers/yeongkwon)

RobotHuman/PCBdefect(원저: Huang & Wei, 2019, Peking University)의 Pascal VOC 어노테이션을 COCO JSON과 YOLO txt 형식으로 변환하고 train/val로 분할한 데이터셋입니다. 이미지와 바운딩 박스 좌표는 원본과 동일합니다. 원본 클래스 폴더별로 파일명을 정렬한 뒤 random.Random(42)로 섞어, 각 클래스의 20%(23장)를 val로, 나머지를 train으로 나눴습니다. - Pascal VOC XML → COCO JSON(annotations/) + YOLO txt(train/, val/) 변환 - 클래스별 8:2 train/val 분할 - Ultralytics용 data.yaml 추가 원 배포처(GitHub, Kaggle)에는 라이선스가 명시되어 있지 않습니다(Kaggle 표기: Unknown). 상업적으로 이용하려면 원저작자의 이용 조건을 별도로 확인하세요.

Publicly accessible mit n<1K

[View dataset](https://savrn.com/datasets/pcb-defect-coco)

Dataset · Object detection

### [sauvc-v1](https://savrn.com/datasets/sauvc-v1)

[Jack Wilson](https://savrn.com/model-publishers/jack-www)

jack-www/sauvc-v1 is the first public Hugging Face publication of the accepted internal capture release v2, generated on 2026-10-10 with rovnext, Isaac Sim 5.1, and OceanSim. The repository name v1 identifies the public publication; it does not refer to the rejected internal v1 capture. All canonical release files retain their original paths and bytes. This card and metadata.jsonl are additive conveniences outside the canonical release manifest. Browse saved images, ground truth, and both legacy model predictions. This separately hosted viewer may be stopped independently of the Hugging Face dataset. There are 4,500 RGB images, 19,647 visible-instance annotations, 50 layouts, and 900…

Publicly accessible mit 1K<n<10K

[View dataset](https://savrn.com/datasets/sauvc-v1)

Dataset · Object detection

### [sugar_beet_weed_detection](https://savrn.com/datasets/sugar-beet-weed-detection)

[AgML](https://savrn.com/model-publishers/project-agml)

PaimioSB2023 is a field dataset for detecting sugar beet seedlings and weeds, collected in 2023 on a 2-hectare research sugar beet field in Hevonpää, Finland, where a commercial FarmDroid FD20 robot was evaluated for seeding and weeding. RGB images from a GoPro action camera mounted 2 m above the ground on the robot, a Parrot Anafi drone flying at 4 m, and a mobile phone were cut into 1280 x 1280 tiles, and each tile was manually annotated with bounding boxes for two classes: sugar beet and weed. The sourceimage column identifies the original photo each tile came from, so tiles from the same photo can be kept in the same split. The dataset contains 18,593 images with 338,171 bounding box…

Publicly accessible cc-by-4.0 10K<n<100K

[View dataset](https://savrn.com/datasets/sugar-beet-weed-detection)

## Other Tasks

- [Text Generation](https://savrn.com/datasets/tasks/text-generation) 139
- [Robotics](https://savrn.com/datasets/tasks/robotics) 117
- [Other](https://savrn.com/datasets/tasks/other) 49
- [Text Classification](https://savrn.com/datasets/tasks/text-classification) 48
- [Text Retrieval](https://savrn.com/datasets/tasks/text-retrieval) 44
- [Question Answering](https://savrn.com/datasets/tasks/question-answering) 39
- [Speech Recognition](https://savrn.com/datasets/tasks/speech-recognition) 33
- [Image to Text](https://savrn.com/datasets/tasks/image-to-text) 29
- [Tabular Classification](https://savrn.com/datasets/tasks/tabular-classification) 22
- [Time Series Forecasting](https://savrn.com/datasets/tasks/time-series-forecasting) 22
- [Image Classification](https://savrn.com/datasets/tasks/image-classification) 17
- [Visual Question Answering](https://savrn.com/datasets/tasks/visual-question-answering) 16

[See all](https://savrn.com/datasets/tasks)