datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ucf_crime# Real-world Anomaly Detection in Surveillance Videos
Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training.
We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work.
# Problem & Motivation
One critical task in video surveillance is detecting anomalous events such as traffic accidents, crimes or illegal activities. Generally, anomalous events rarely occur as compared to normal activities. Therefore, to alleviate the waste of labor and time, developing intelligent computer vision algorithms for automatic video anomaly detection is a pressing need. The goal of a practical anomaly detection system is to timely signal an activity that deviates normal patterns and identify the time window of the occurring anomaly. Therefore, anomaly detection can be considered as coarse level video understanding, which filters out anomalies from normal patterns. Once an anomaly is detected, it can further be categorized into one of the specific activities using classification techniques.
In this work, we propose an anomaly detection algorithm using weakly labeled training videos. That is we only know the video-level labels, i.e. a video is normal or contains anomaly somewhere, but we do not know where. This is intriguing because we can easily annotate a large number of videos by only assigning video-level labels. To formulate a weakly-supervised learning approach, we resort to multiple instance learning. Specifically, we propose to learn anomaly through a deep MIL framework by treating normal and anomalous surveillance videos as bags and short segments/clips of each video as instances in a bag. Based on training videos, we automatically learn an anomaly ranking model that predicts high anomaly scores for anomalous segments in a video. During testing, a longuntrimmed video is divided into segments and fed into our deep network which assigns anomaly score for each video segment such that an anomaly can be detected.
# Method
Our proposed approach (summarized in Figure 1) begins with dividing surveillance videos into a fixed number of segments during training. These segments make instances in a bag. Using both positive (anomalous) and negative (normal) bags, we train the anomaly detection model using the proposed deep MIL ranking loss.
https://www.crcv.ucf.edu/projects/real-world/method.png
# UCF-Crime Dataset
We construct a new large-scale dataset, called UCF-Crime, to evaluate our method. It consists of long untrimmed surveillance videos which cover 13 realworld anomalies, including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety. We compare our dataset with previous anomaly detection datasets in Table 1. For more details about the UCF-Crime dataset, please refer to our paper. A short description of each anomalous event is given below.
Abuse: This event contains videos which show bad, cruel or violent behavior against children, old people, animals, and women.
Burglary: This event contains videos that show people (thieves) entering into a building or house with the intention to commit theft. It does not include use of force against people.
Robbery: This event contains videos showing thieves taking money unlawfully by force or threat of force. These videos do not include shootings.
Stealing: This event contains videos showing people taking property or money without permission. They do not include shoplifting.
Shooting: This event contains videos showing act of shooting someone with a gun.
Shoplifting: This event contains videos showing people stealing goods from a shop while posing as a shopper.
Assault: This event contains videos showing a sudden or violent physical attack on someone. Note that in these videos the person who is assaulted does not fight back.
Fighting: This event contains videos displaying two are more people attacking one another.
Arson: This event contains videos showing people deliberately setting fire to property.
Explosion: This event contains videos showing destructive event of something blowing apart. This event does not include videos where a person intentionally sets a fire or sets off an explosion.
Arrest: This event contains videos showing police arresting individuals.
Road Accident: This event contains videos showing traffic accidents involving vehicles, pedestrians or cyclists.
Vandalism: This event contains videos showing action involving deliberate destruction of or damage to public or private property. The term includes property damage, such as graffiti and defacement directed towards any property without permission of the owner.
Normal Event: This event contains videos where no crime occurred. These videos include both indoor (such as a shopping mall) and outdoor scenes as well as day and night-time scenes.
https://www.crcv.ucf.edu/projects/real-world/dataset_table.png
https://www.crcv.ucf.edu/projects/real-world/method.pngucf-crimeucf-crime-tencrop-i3ducf-crime-clip-featuresucf-crime-processed-featuresucf_crime
UCF-Crime Dataset (WebDataset Format)
이 데이터셋은 UCF-Crime 데이터셋을 Hugging Face datasets 라이브러리의 스트리밍 기능을 위해 WebDataset(TAR Sharding) 형식으로 변환한 버전입니다.
100GB 이상의 대규모 비디오 데이터를 효율적으로 로드할 수 있습니다.
from datasets import load_dataset
# Anomaly Detection 설정 로드
dataset = load_dataset("your_id/ucf_crime", "anomaly_detection", split="train", streaming=True)
for sample in dataset:
video_path = sample["mp4"]
label = sample["json"]["event"]
print(f"Video loaded: {video_path}, Label:… See the full description on the dataset page: https://huggingface.co/datasets/AllenXeon/ucf_crime.UCF-Crimehf download backseollgi/UCF-Crime --repo-type dataset --local-dir .
cat UCF_Crimes.zip.part_* > UCF_Crimes.zip
unzip UCF_Crimes.zip
ucf_crime_tencrop_i3d_seg32Train : TenCrop + I3D_8X8_R50(pytorchvideo) + Segment(32)
Test : TenCrop + I3D_8X8_R50(pytorchvideo)
bangla-crime-investigation-patterns-v2
Bangla Crime Investigation Patterns V2
This dataset is an anonymized, structured extraction from 1,078 public Bangla crime-investigation video transcripts. It was built to support machine-learning and LLM research on crime-pattern information extraction, case summarization, event sequencing, entity/relationship extraction, and Bengali investigative-report analysis.
The public release does not include raw full transcripts. It contains short redacted evidence snippets linked to… See the full description on the dataset page: https://huggingface.co/datasets/tanziro/bangla-crime-investigation-patterns-v2.UCF-crime-binary # UCF Video Dataset
This is a video dataset structured for training, validation, and testing purposes.
## Structure
- `train/normal`: Normal videos for training
- `train/abnormal`: Abnormal videos for training
- `test/normal`: Normal videos for testing
- `test/abnormal`: Abnormal videos for testing
- `val/normal`: Normal videos for validation
- `val/abnormal`: Abnormal videos for validation
## Usage
This dataset can be loaded using the Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/shahadalll/UCF-crime-binary.ufc-crime-videosUCF_Crime_ICML_annotationhf download backseollgi/UCF_Crime_ICML_annotation --repo-type dataset --local-dir . --exclude README.md --exclude .gitattributes
ucf-crimecrime_and_punish
Dataset Card for "crime_and_punish"
Dataset Summary
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
crime-and-punish
Size of downloaded dataset files: 1.21 MB
Size of the generated dataset: 1.27 MB
Total amount of disk used: 2.47 MB
An example of 'train' looks as follows.
{
"line": "CRIME AND PUNISHMENT\n"
}
Data Fields
The… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/crime_and_punish.UCF-Crimechicago_crimeDataset title: Chicago Crime Dataset.
Dataset authors: Julia Moska, Oleksii Furman, Kacper Kozaczko, Szymon Leszkiewicz, Jakub Polczyk, Piotr Gramacki, Piotr Szymański - Wrocław University of Science and Technology
Data authors: The data comes from the CLEAR (Citizen Law Enforcement Analysis and Reporting) system of the Chicago Police Department.
Data creation and/or collection date: From January 2020 to December 2022. A subset from the year 2022 was utilized for the benchmark.
Research… See the full description on the dataset page: https://huggingface.co/datasets/kraina/chicago_crime.lavad_ucf_crime_featureshf download backseollgi/lavad_ucf_crime_features --repo-type dataset --local-dir .
unzip ucf_crime.zip
abbas829_global-organized-crime-index-20212025
Global Organized Crime Index (2021–2025)
Global Organized Crime Index dataset for year 2021, 2023 & 2025
Dataset Info
Source: Kaggle
Original Size: 0.04 MB
Kaggle Downloads: 47
Files: 1
Files
global_oc_index.csv
Mirrored from Kaggle
NC_CrimeThe dataset, compiled from public police incident reports across various cities in North Carolina, covers a period from the early 2000s through to 2024. It is intended to facilitate the study of crime trends and patterns.philadelphia_crimeDataset title: Philadelphia Crimes Dataset.
Dataset authors: Julia Moska, Oleksii Furman, Kacper Kozaczko, Szymon Leszkiewicz, Jakub Polczyk, Piotr Gramacki, Piotr Szymański - Wrocław University of Science and Technology
Data authors: The data originates from the OpenDataPhilly portal and is provided under the City of Philadelphia license.
Collection date: The utilized subset of data is from the year 2023.
Research methodology and tools used for data acquisition: The dataset contains reported… See the full description on the dataset page: https://huggingface.co/datasets/kraina/philadelphia_crime.HIVAU-70k_UCF-crime
다운받아서 압축해제 후 사용하세요
현재위치에 다운
hf download backseollgi/HIVAU-70k_UCF-crime --repo-type dataset --local-dir .
cat ucf-crime.tar.part_* | tar -xvf -
ux-crime-scene-traces
🔎 UX Crime Scene — Investigation Traces
Real agent traces from UX Crime Scene,
a film-noir detective that investigates UI screenshots as crime scenes — built for the
Build Small Hackathon (Gradio × Hugging Face).
Each row is one real investigation: the input screenshot the user dropped, and the
raw structured verdict Qwen2.5-VL-7B returned — the crimes it found, the bounding
box of each guilty element, the testimony, the severity, and the final grade.
The set spans different… See the full description on the dataset page: https://huggingface.co/datasets/build-small-hackathon/ux-crime-scene-traces.UCF-Crime-DatasetUCF_crime_extract_eventpolice-crime-london
Street-level crime, outcomes and stop & search — London
Every crime and anti-social-behaviour incident recorded by London's two territorial police forces (the Metropolitan Police Service and City of London Police) since December 2010, with case outcomes and stop & search records, as published on data.police.uk. Full history is reconstructed from data.police.uk's permanent monthly archive zips, each month taken from the newest zip that contains it (the most-revised version).… See the full description on the dataset page: https://huggingface.co/datasets/jhumbl/police-crime-london.UCF-Crime-Dataset
UCF Crime Dataset
About Dataset
The dataset contains extracted images from the UCF crime dataset used for Real-world Anomaly Detection in Surveillance Videos
Content
The dataset contains images extracted from every video from the UCF Crime Dataset.
Every 10th frame is extracted from each full-length video and combined for every video in that class.
All the images are of size 64*64 and in .png format
The dataset has a total of 14 Classes :
1. Abuse
2. Arrest
3.… See the full description on the dataset page: https://huggingface.co/datasets/hibana2077/UCF-Crime-Dataset.uof-grapheme-crime-scenes
UOF Grapheme Segmentation Scenes v2
Synthetic extended-grapheme cases with escaped input, cluster targets, code-point counts, UTF-8 byte counts, and cursor/backspace step estimates.
Segmentation uses the regex \X reference with version 2025.11.3. Cluster strings are stored in escaped form so that combining marks, joiners, variation selectors, and supplementary-plane characters remain inspectable in JSON.
Release contract
Generator: uof-frontier-v2.0.0
Regex… See the full description on the dataset page: https://huggingface.co/datasets/NewSonnet/uof-grapheme-crime-scenes.dread-crime-forum
Dread Forum Archive
A near-complete capture of public content from Dread, a Reddit-style discussion forum hosted as a Tor hidden service. Dread is one of the longest-running darknet community forums and a primary site for discussion of darknet markets, operational security, cryptocurrency, and related topics.
The archive covers content posted between April 2018 and September 2025 and is structured as three Parquet-backed splits: posts, comments, and users (with parsed PGP key… See the full description on the dataset page: https://huggingface.co/datasets/trentmkelly/dread-crime-forum.CrimeX
CRIMEX v1 — LA Incident Behavioral Intelligence Dataset
📌 Overview
CRIMEX v1 is a large-scale enriched criminal incident dataset derived from the Los Angeles Open Crime Data.
It transforms raw incident records into a behavioral intelligence dataset by adding multiple feature layers:
Temporal intelligence
Behavioral profiling
Contextual risk signals
Graph-inspired features
Data quality and explainability metrics
The dataset is designed for machine learning, crime… See the full description on the dataset page: https://huggingface.co/datasets/aymansharara/CrimeX.crime_judgement