Datasets:

simplexsigil2
/

wanfall

@@ -1,14 +1,7 @@
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-      "Read(//lsdf/users/dschneider-kf3609/workspace/PROJECTS/wanfall/labels/**)",
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-      "WebSearch"
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+    ]
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.gitignore CHANGED Viewed

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-CLAUDE.md
-create_demographic_plots.py
-create_splits.py
-export_via_to_csv.py
-extract_jsonl_metadata.py
-test_wanfall_builder.py
-.claude
-test_framewise_complete.py

+convert_oops_via_to_csv.py
+# Symlink for local testing (HF derives dataset name from directory name)
+hf.py
+__pycache__

README.md CHANGED Viewed

@@ -5,468 +5,363 @@ task_categories:
 language:
 - en
 tags:
-- synthetic
-- activity-recognition
 - fall-detection
-pretty_name: 'WanFall: A Synthetic Activity Recognition Dataset'
 size_categories:
 - 10K<n<100K
-configs:
-  - config_name: labels
-    data_files:
-      - labels/wanfall.csv
-    description: "All temporal segment labels (19,228 segments) in a single split."
-  - config_name: metadata
-    data_files:
-      - videos/metadata.csv
-    description: "Video level metadata."
-  - config_name: random
-    data_files:
-      - split: train
-        path: "splits/random/train.csv"
-      - split: validation
-        path: "splits/random/val.csv"
-      - split: test
-        path: "splits/random/test.csv"
-    default: true
-    description: "Random 80/10/10 train/val/test split (seed 42)"
-  - config_name: cross_age
-    data_files:
-      - split: train
-        path: "splits/cross_age/train.csv"
-      - split: validation
-        path: "splits/cross_age/val.csv"
-      - split: test
-        path: "splits/cross_age/test.csv"
-    description: "Cross-age evaluation: train on young/middle-aged adults, val on teenagers, test on children/toddlers/elderly"
-  - config_name: cross_ethnicity
-    data_files:
-      - split: train
-        path: "splits/cross_ethnicity/train.csv"
-      - split: validation
-        path: "splits/cross_ethnicity/val.csv"
-      - split: test
-        path: "splits/cross_ethnicity/test.csv"
-    description: "Cross-ethnicity evaluation: train on white/asian/hispanic, test on black/mena/nhpi"
-  - config_name: cross_bmi
-    data_files:
-      - split: train
-        path: "splits/cross_bmi/train.csv"
-      - split: validation
-        path: "splits/cross_bmi/val.csv"
-      - split: test
-        path: "splits/cross_bmi/test.csv"
-    description: "Cross-BMI evaluation: train on normal/underweight, val on overweight, test on obese"
-  - config_name: framewise
-    description: "Frame-wise labels (81 per video). Use framewise=True parameter with any split config instead."
 ---
-[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
-# WanFall: A Synthetic Activity Recognition Dataset
-Synthetic activity recognition dataset with 12,000 videos focused on fall detection and activities of daily living. Features rich demographic metadata and multiple evaluation protocols for bias analysis.
-**Status:** Under active development, subject to change.
-## Dataset Statistics
-| Property | Value |
-|----------|-------|
-| **Videos** | 12,000 (5.0625s each) |
-| **Temporal Segments** | 19,228 |
-| **Activity Classes** | 16 |
-| **Frames per Video** | 81 frames @ 16fps |
-| **Annotation Formats** | Temporal segments OR frame-wise labels |
-| **Metadata Fields** | 12 (6 demographic + 6 scene) |
-## Quick Start
-```python
-from datasets import load_dataset
-# Random split with temporal segments (default)
-dataset = load_dataset("simplexsigil2/wanfall", "random")
-print(f"Train: {len(dataset['train'])} segments")  # 15,344 segments
-# Random split with frame-wise labels (81 per video)
-dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
-print(f"Train: {len(dataset['train'])} videos")    # 9,600 videos
-# Cross-demographic evaluation
-cross_age = load_dataset("simplexsigil2/wanfall", "cross_age")
-# Access example
-example = dataset['train'][0]
-print(f"Video: {example['path']}")
-print(f"Activity: {example['label']} ({example['start']:.2f}s - {example['end']:.2f}s)")
-print(f"Demographics: {example['age_group']}, {example['race_ethnicity_omb']}")
-```
-## Activity Classes
-16 activity classes covering falls, posture transitions, and static states:
-```python
-LABEL_MAP = {
-    0: "walk",        # Walking movement, including jogging and running
-    1: "fall",        # Falling down action (loss of control)
-    2: "fallen",      # Person on ground after fall
-    3: "sit_down",    # Transition from standing to sitting
-    4: "sitting",     # Stationary sitting posture
-    5: "lie_down",    # Intentionally lying down (not falling)
-    6: "lying",       # Stationary lying posture
-    7: "stand_up",    # Getting up (to sitting or standing)
-    8: "standing",    # Stationary standing posture
-    9: "other",       # Unclassified activities
-    10: "kneel_down", # Transition to kneeling
-    11: "kneeling",   # Stationary kneeling posture
-    12: "squat_down", # Transition to squatting
-    13: "squatting",  # Stationary squatting posture
-    14: "crawl",      # Crawling movement on hands and knees
-    15: "jump",       # Jumping action
-}
-```
-**Motion Types:**
-- **Dynamic** (0-3, 5, 7, 9-10, 12, 14-15): Transitions and movements
-- **Static** (2, 4, 6, 8, 11, 13): Stationary postures
-## Data Format
-### CSV Columns (19 fields)
-```python
-# Core annotation fields
-path                    # Video path (e.g., "fall/fall_ch_001")
-label                   # Activity class ID (0-15)
-start                   # Segment start time (seconds)
-end                     # Segment end time (seconds)
-subject                 # -1 (synthetic data)
-cam                     # -1 (single view)
-dataset                 # "wanfall"
-# Demographic metadata (6 fields)
-age_group               # toddlers_1_4, children_5_12, teenagers_13_17, young_adults_18_34, middle_aged_35_64, elderly_65_plus
-gender_presentation     # male, female
-monk_skin_tone          # mst1-mst10 (Monk Skin Tone scale)
-race_ethnicity_omb      # white, black, asian, hispanic_latino, aian, nhpi, mena (OMB categories)
-bmi_band                # underweight, normal, overweight, obese
-height_band             # short, avg, tall
-# Scene metadata (6 fields)
-environment_category    # indoor, outdoor
-camera_shot             # static_wide, static_medium_wide
-speed                   # 24fps_rt, 25fps_rt, 30fps_rt, std_rt
-camera_elevation        # eye, low, high, top
-camera_azimuth          # front, rear, left, right
-camera_distance         # medium, far
-```
-**References:**
-- [Monk Skin Tone Scale](https://skintone.google/the-scale) - 10-point inclusive skin tone representation
-- [OMB Race/Ethnicity Standards](https://www.census.gov/newsroom/blogs/random-samplings/2024/04/updates-race-ethnicity-standards.html)
-## Split Configurations
-### 1. Random Split (80/10/10)
-Standard baseline with random video assignment (seed 42).
-| Split | Videos | Segments |
-|-------|--------|----------|
-| Train | 9,600 | 15,344 |
-| Val | 1,200 | 1,956 |
-| Test | 1,200 | 1,928 |
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "random")
-```
-### 2. Cross-Age Split
-Evaluates generalization across age groups. Train on adults, test on children and elderly.
-| Split | Videos | Age Groups |
-|-------|--------|------------|
-| **Train** | 4,000 | `young_adults_18_34` (2,000)<br>`middle_aged_35_64` (2,000) |
-| **Val** | 2,000 | `teenagers_13_17` (2,000) |
-| **Test** | 6,000 | `children_5_12` (2,000)<br>`toddlers_1_4` (2,000)<br>`elderly_65_plus` (2,000) |
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "cross_age")
-```
-### 3. Cross-Ethnicity Split
-Evaluates generalization across racial/ethnic groups with maximum phenotypic distance. Train on White/Asian/Hispanic, test on Black/MENA/NHPI.
-| Split | Videos | Ethnicities |
-|-------|--------|-------------|
-| **Train** | 5,178 | `white` (1,709)<br>`asian` (1,691)<br>`hispanic_latino` (1,778) |
-| **Val** | 1,741 | `aian` (1,741) |
-| **Test** | 5,081 | `black` (1,684)<br>`mena` (1,680)<br>`nhpi` (1,717) |
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "cross_ethnicity")
-```
-### 4. Cross-BMI Split
-Evaluates generalization across body types. Train on normal/underweight, test on obese.
-| Split | Videos | BMI Bands |
-|-------|--------|-----------|
-| **Train** | 6,066 | `normal` (3,040)<br>`underweight` (3,026) |
-| **Val** | 2,962 | `overweight` (2,962) |
-| **Test** | 2,972 | `obese` (2,972) |
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "cross_bmi")
 ```
-**Note:** All cross-demographic splits contain the same videos, just organized differently. Total unique videos: 12,000.
-## Usage
-The dataset provides flexible loading options depending on your use case. The key distinction is between **segment-level** and **video-level** samples.
-### Loading Modes Overview
-| Mode | Sample Unit | Has start/end? | Has frame_labels? | Random Split Train Size |
-|------|-------------|----------------|-------------------|------------------------|
-| **Temporal Segments** | Segment | ✅ Yes | ❌ No | 15,344 segments (9,600 videos) |
-| **Frame-Wise Labels** | Video | ❌ No | ✅ Yes (81 labels) | 9,600 videos |
-### 1. Temporal Segments (Default)
-Load temporal segment annotations where **each sample is a segment** with start/end times. Multiple segments can come from the same video.
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "random")
-# Each example is a SEGMENT (not a video)
-example = dataset['train'][0]
-print(example['path'])      # "fall/fall_ch_001"
-print(example['label'])     # 1 (activity class ID)
-print(example['start'])     # 0.0 (start time in seconds)
-print(example['end'])       # 1.006 (end time in seconds)
-print(example['age_group']) # Demographic metadata
-# Dataset contains multiple segments per video
-print(f"Total segments in train: {len(dataset['train'])}")  # 15,344
-print(f"Unique videos: {len(set([ex['path'] for ex in dataset['train']]))}") # 9,600
 ```
-**Use case:** Training models on activity classification where you want to extract and process only the relevant video segment for each activity.
-### 2. Frame-Wise Labels
-Load dense frame-level labels where **each sample is a video** with 81 frame labels. Each video appears exactly once.
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
-# Each example is a VIDEO (not a segment)
-example = dataset['train'][0]
-print(example['path'])         # "fall/fall_ch_001"
-print(example['frame_labels']) # [1, 1, 1, ..., 11, 11] (81 labels)
-print(len(example['frame_labels']))  # 81 frames
-print(example['age_group'])    # Demographic metadata included
-# Dataset contains one sample per video
-print(f"Total videos in train: {len(dataset['train'])}")  # 9,600 videos
 ```
-**Use case:** Training sequence models (e.g., temporal action segmentation) that process entire videos and predict frame-level labels.
-**Key features:**
-- Works with all split configs: Add `framewise=True` to any split
-- Efficient: 348KB compressed archive, automatically cached
-- Complete metadata: All demographic attributes included
-### 3. Additional Configurations
-```python
-# All segments without train/val/test splits
-dataset = load_dataset("simplexsigil2/wanfall", "labels")  # 19,228 segments
-# Video metadata only (no labels)
-dataset = load_dataset("simplexsigil2/wanfall", "metadata")  # 12,000 videos
-# Paths only (minimal memory footprint)
-dataset = load_dataset("simplexsigil2/wanfall", "random", paths_only=True)
 ```
-### Practical Examples
-#### Label Conversion
-Labels are stored as integers (0-15) but can be converted to strings:
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "random")
-label_feature = dataset['train'].features['label']
-# Convert integer to string
-label_name = label_feature.int2str(1)      # "fall"
-# Convert string to integer
-label_id = label_feature.str2int("walk")   # 0
-# Access all label names
-all_labels = label_feature.names           # ['walk', 'fall', 'fallen', ...]
-```
-#### Filter by Demographics
-```python
-dataset = load_dataset("simplexsigil2/wanfall", "labels")
-segments = dataset['train']
-# Filter elderly fall segments
-elderly_falls = [
-    ex for ex in segments
-    if ex['age_group'] == 'elderly_65_plus' and ex['label'] == 1
-]
-print(f"Found {len(elderly_falls)} elderly fall segments")
-# Filter by multiple demographics
-indoor_male_falls = [
-    ex for ex in segments
-    if ex['environment_category'] == 'indoor'
-    and ex['gender_presentation'] == 'male'
-    and ex['label'] == 1
-]
-```
-#### Cross-Demographic Evaluation
-```python
-# Train on young adults, test on children and elderly
-cross_age = load_dataset("simplexsigil2/wanfall", "cross_age", framewise=True)
-# Train contains only: young_adults_18_34, middle_aged_35_64
-for example in cross_age['train'][:5]:
-    print(f"Train video: {example['path']}, age: {example['age_group']}")
-# Test contains: children_5_12, toddlers_1_4, elderly_65_plus
-for example in cross_age['test'][:5]:
-    print(f"Test video: {example['path']}, age: {example['age_group']}")
-```
-#### Training Loop Example
 ```python
 from datasets import load_dataset
-import torch
-# Load dataset with frame-wise labels
-dataset = load_dataset("simplexsigil2/wanfall", "random", framewise=True)
-for epoch in range(num_epochs):
-    for example in dataset['train']:
-        video_path = example['path']
-        frame_labels = torch.tensor(example['frame_labels'])  # (81,)
-        # Load video frames (user must implement)
-        # frames = load_video(video_root / f"{video_path}.mp4")  # (81, H, W, 3)
-        # Forward pass
-        # outputs = model(frames)
-        # loss = criterion(outputs, frame_labels)
-        # loss.backward()
 ```
-## Annotation Guidelines
-### Temporal Precision
-Annotations use sub-second accuracy with decimal timestamps (e.g., `start: 0.0, end: 1.006`). Most frames in videos are labeled, with minimal gaps between activities.
-### Activity Sequences
-Videos contain natural transitions between activities. Common sequences include:
 ```
-walk → fall → fallen → stand_up
-walk → sit_down → sitting → stand_up
-walk → lie_down → lying → stand_up
-standing → squat_down → squatting → stand_up
-```
-Not all transitions include static states. For example, a person might `stand_up` immediately after falling without a `fallen` state.
-### Motion Types
-**Dynamic Actions** (transitions and movements):
-- Labeled from the **first frame** where the motion begins
-- End when the person reaches a **resting state** or begins a new action
-- If one motion is followed by another, the transition occurs at the first frame showing movement not explained by the previous action
-**Static States** (stationary postures):
-- Begin when person **comes to rest** in that posture
-- Continue until the next motion begins
-- Example for `sitting`: Does not start when the body touches the chair, but when the body loses its tension and settles into the seated position
-### Label Boundaries
-- **Dynamic → Dynamic**: Transition at first frame of new motion
-- **Dynamic → Static**: Static begins when movement stops and body settles
-- **Static → Dynamic**: Dynamic begins at first frame of movement
-## Demographic Distribution
-Rich demographic and scene metadata enables bias analysis and cross-demographic evaluation.
-![Demographic Overview](figures/demographic_overview.png)
-**Note:** Metadata represents generation prompts. Due to generative model biases, actual visual attributes may deviate, particularly for ethnicity and body type. Age and gender are generally more reliable.
-**Scene Variations:**
-- Environments: Indoor/outdoor settings
-- Camera angles: 4 elevations × 4 azimuths × 2 distances
-- Shot types: Static wide and medium-wide
-## Video Data
-**Videos are NOT included in this repository.** This dataset contains only annotations and metadata.
-### Video Specifications
-- **Duration:** 5.0625 seconds per clip
-- **Frame count:** 81 frames
-- **Frame rate:** 16 fps
-- **Format:** MP4 (H.264)
-- **Resolution:** Variable (synthetic generation)
-### Accessing Videos
-Videos will be released at a later point in time. Information about access will be provided here when available.
-When videos become available, they should be organized with the following structure:
-```
-video_root/
-├── fall/
-│   ├── fall_ch_001.mp4
-│   ├── fall_ch_002.mp4
-│   └── ...
-├── fallen/
-│   ├── fallen_ch_001.mp4
-│   └── ...
-└── ...
-```
-The `path` field in the CSV corresponds to the relative path without the `.mp4` extension (e.g., `"fall/fall_ch_001"` → `video_root/fall/fall_ch_001.mp4`).
-## License
-[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
-Annotations and metadata released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Video data is synthetic and subject to separate terms.

 language:
 - en
 tags:
+- medical
 - fall-detection
+- activity-recognition
+- synthetic
+pretty_name: 'OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection'
 size_categories:
 - 10K<n<100K
 ---
+[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+</br>
+<a href="https://arxiv.org/abs/2505.19889">
+  <img src="https://img.shields.io/badge/arXiv-2505.19889-b31b1b.svg?logo=arXiv" alt="arXiv" style="height:20px;">
+</a>
+</br>
+<a href="https://simplexsigil.github.io/omnifall/">
+  <img src="https://img.shields.io/badge/Omnifall-Project_Page-2FA7C9.svg" alt="arXiv" style="height:20px;">
+</a>
+# OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection
+This repository contains annotations, splits, and metadata for OmniFall, a comprehensive benchmark with three components:
+- **OF-Staged (OF-Sta):** 8 public staged fall detection datasets (~14h single-view, 112 subjects, 31 camera views)
+- **OF-In-the-Wild (OF-ItW):** Curated genuine accident videos from OOPS (~2.7h, 818 videos)
+- **OF-Synthetic (OF-Syn):** 12,000 synthetic videos generated with Wan 2.2 (~17h) with controlled demographic diversity
+All components share a sixteen-class activity taxonomy. Staged datasets use the core ten classes (0-9), while OF-ItW and OF-Syn use the full sixteen classes (0-15).
+[OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection](https://arxiv.org/abs/2505.19889)
+Also have a look for additional information on our project page:
+[OmniFall Project Page](https://simplexsigil.github.io/omnifall/)
+## Datasets
+### OF-Staged (8 public datasets)
+1. **[CMDFall](https://www.mica.edu.vn/perso/Tran-Thi-Thanh-Hai/CMDFALL.html)** (7h 25m single view) - 50 subjects, 7 synchronized views
+2. **[UP Fall](https://sites.google.com/up.edu.mx/har-up/)** (4h 35m) - 17 subjects, 2 synchronized views
+3. **[Le2i](https://search-data.ubfc.fr/imvia/FR-13002091000019-2024-04-09_Fall-Detection-Dataset.html)** (47m) - 9 subjects, 6 different rooms
+4. **[GMDCSA24](https://github.com/ekramalam/GMDCSA24-A-Dataset-for-Human-Fall-Detection-in-Videos)** (21m) - 4 subjects, 3 rooms
+5. **[CAUCAFall](https://data.mendeley.com/datasets/7w7fccy7ky/4)** (16m) - 10 subjects, 1 room
+6. **[EDF](https://doi.org/10.5281/zenodo.15494102)** (13m) - 5 subjects, 2 views synchronized
+7. **[OCCU](https://doi.org/10.5281/zenodo.15494102)** (14m) - 5 subjects, 2 views not synchronized
+8. **[MCFD](https://www.iro.umontreal.ca/~labimage/Dataset/)** (12m) - 1 subject, 8 views
+### OF-ItW (In-the-Wild)
+9. **[OOPS-Fall](https://oops.cs.columbia.edu/data/)** (2h 39m) - Curated subset of genuine fall accidents from the OOPS dataset, strong variation in subjects and views.
+### OF-Syn (Synthetic)
+10. **OF-Syn** (16h 53m) - 12,000 synthetic videos generated with Wan 2.2 video diffusion model. Features controlled demographic diversity across age (6 groups), ethnicity (7 OMB categories), body type (4 BMI bands), gender, and environments. Labels include 19 columns: 7 core annotation fields plus 12 demographic and scene metadata fields.
+## Structure
+The repository is organized as follows:
+- `omnifall.py` - Custom HuggingFace dataset builder (handles all configs)
+- `labels/` - CSV files containing temporal segment annotations
+  - Staged/OOPS labels: 7 columns (`path, label, start, end, subject, cam, dataset`)
+  - OF-Syn labels: 19 columns (7 core + 12 demographic/scene metadata)
+- `splits/` - Train/validation/test splits
+  - `splits/cs/` - Cross-subject splits (staged + OOPS)
+  - `splits/cv/` - Cross-view splits (staged + OOPS)
+  - `splits/syn/` - Synthetic splits (random, cross_age, cross_ethnicity, cross_bmi)
+- `videos/metadata.csv` - OF-Syn video-level metadata (12,000 videos)
+- `data_files/syn_frame_wise_labels.tar.zst` - OF-Syn frame-wise HDF5 labels
+### Label Format
+Each label file in the `labels/` directory follows this format:
 ```
+path,label,start,end,subject,cam,dataset
+path/to/clip,class_id,start_time,end_time,subject_id,camera_id,dataset_name
 ```
+Where:
+- `path`: Relative path to the video, given the respective dataset root.
+- `label`: Class ID (0-15) corresponding to one of sixteen activity classes:
+  - 0: walk
+  - 1: fall
+  - 2: fallen
+  - 3: sit_down
+  - 4: sitting
+  - 5: lie_down
+  - 6: lying
+  - 7: stand_up
+  - 8: standing
+  - 9: other
+  - 10: kneel_down
+  - 11: kneeling
+  - 12: squat_down
+  - 13: squatting
+  - 14: crawl
+  - 15: jump
+- `start`: Start time of the segment (in seconds)
+- `end`: End time of the segment (in seconds)
+- `subject`: Subject ID
+- `cam`: Camera view ID
+- `dataset`: Name of the dataset
+**Note on class usage:** Staged datasets (OF-Sta) use only the core ten classes (0-9). OF-ItW and OF-Syn use the full sixteen-class taxonomy. The extended classes (10-15) are infrequent and can be treated as "other" (class 9) for compatibility.
+Cam and subject IDs are -1 for OF-ItW and OF-Syn, since these datasets have no consistent camera views or subjects across videos.
+**Note on OF-Syn labels:** The `labels/of-syn.csv` file contains 19 columns: the 7 core fields above plus 12 demographic and scene metadata fields (age_group, gender_presentation, monk_skin_tone, race_ethnicity_omb, bmi_band, height_band, environment_category, camera_shot, speed, camera_elevation, camera_azimuth, camera_distance). These additional fields are only available when loading OF-Syn configs.
+### Split Format
+Split files in the `splits/` directory list the video segments included in each partition. You can use the split paths to filter the label data.:
 ```
+path
+path/to/clip
 ```
+## Evaluation Protocols
+All configurations are defined in the `omnifall.py` dataset builder and loaded via `load_dataset("simplexsigil2/omnifall", "<config_name>")`.
+### Labels (no train/val/test splits)
+- `labels` (default): All staged + OOPS labels (52k segments, 7 columns)
+- `labels-syn`: OF-Syn labels with demographic metadata (19k segments, 19 columns)
+- `metadata-syn`: OF-Syn video-level metadata (12k videos)
+- `framewise-syn`: OF-Syn frame-wise HDF5 labels (81 labels per video)
+### OF-Staged Configs
+- `of-sta-cs`: 8 staged datasets, cross-subject splits
+- `of-sta-cv`: 8 staged datasets, cross-view splits
+### OF-ItW Config
+- `of-itw`: OOPS-Fall in-the-wild genuine accidents
+### OF-Syn Configs
+- `of-syn`: Fixed randomized 80/10/10 split
+- `of-syn-cross-age`: Cross-age split (train: adults, test: children/elderly)
+- `of-syn-cross-ethnicity`: Cross-ethnicity split
+- `of-syn-cross-bmi`: Cross-BMI split (train: normal/underweight, test: obese)
+### Cross-Domain Evaluation
+- `of-sta-itw-cs`: Train/val on staged CS, test on OOPS
+- `of-sta-itw-cv`: Train/val on staged CV, test on OOPS
+- `of-syn-itw`: Train/val on OF-Syn random, test on OOPS
+### Aggregate Configs (staged + OOPS combined)
+- `cs`: Cross-subject splits for all staged + OOPS
+- `cv`: Cross-view splits for all staged + OOPS
+### Individual Dataset Configs
+- `caucafall`, `cmdfall`, `edf`, `gmdcsa24`, `le2i`, `mcfd`, `occu`, `up_fall`: Individual datasets with cross-subject splits
+### Deprecated Config Names
+The following old config names still work but emit a deprecation warning:
+| Old Name | Use Instead |
+|---|---|
+| `cs-staged` | `of-sta-cs` |
+| `cv-staged` | `of-sta-cv` |
+| `cs-staged-wild` | `of-sta-itw-cs` |
+| `cv-staged-wild` | `of-sta-itw-cv` |
+| `OOPS` | `of-itw` |
+## Examples
 ```python
 from datasets import load_dataset
+# Load the three main components
+staged = load_dataset("simplexsigil2/omnifall", "of-sta-cs")   # 8 staged datasets
+itw    = load_dataset("simplexsigil2/omnifall", "of-itw")      # OOPS in-the-wild
+syn    = load_dataset("simplexsigil2/omnifall", "of-syn")      # OF-Syn synthetic
+# Each returns train/validation/test splits with labels already merged
+print(f"Staged train: {len(staged['train'])} segments")
+print(f"ItW test: {len(itw['test'])} segments")
+print(f"Syn train: {len(syn['train'])} segments")
+# Synthetic data includes demographic metadata (19 columns)
+example = syn['train'][0]
+print(f"Path: {example['path']}, Label: {example['label']}")
+print(f"Age: {example['age_group']}, Ethnicity: {example['race_ethnicity_omb']}")
+# Cross-domain: train on staged, test on wild
+cross = load_dataset("simplexsigil2/omnifall", "of-sta-itw-cs")
+print(f"Train on staged: {len(cross['train'])} segments")
+print(f"Test on wild: {len(cross['test'])} segments")
+# All labels without splits (for custom splitting)
+labels = load_dataset("simplexsigil2/omnifall", "labels")["train"]
+syn_labels = load_dataset("simplexsigil2/omnifall", "labels-syn")["train"]
+# Synthetic frame-wise labels (81 labels per video)
+framewise = load_dataset("simplexsigil2/omnifall", "of-syn", framewise=True)
+print(f"Frame labels: {framewise['train'][0]['frame_labels'][:5]}...")  # [1, 1, 1, ...]
 ```
+## Label definitions
+In this section we provide additional information about the labelling process to provide as much transparency as possible.
+The benchmark uses a sixteen-class taxonomy. Staged datasets use classes 0-9, while OOPS-Fall uses the full range 0-15.
+### Core Classes (0-9, all datasets)
+- **`0|walk`** - Move around, including jogging and running and "drunk walking", but only if it is not part of some special exercise like pulling your knees up. Not when pushing a large object like a chair, but included carrying something small like an apple.
+- **`1|fall`** - The act of falling (from any previous state). Includes falling on a bed, if the process is not a controlled lying down with arms as support.
+- **`2|fallen`** - Being on the ground or a mattress after a fall.
+- **`3|sit_down`** - Sitting down on bed or chair or ground.
+- **`4|sitting`** - Sitting on bed or chair or ground.
+- **`5|lie_down`** - Lying down intentionally (in contrast to a fall) on ground or bed.
+- **`6|lying`** - Being in a lying position (in bed or on the ground) after intentionally getting into that position.
+- **`7|stand_up`** - Standing up from a fallen state, from lying or sitting. Includes getting from lying position into sitting position.
+- **`8|standing`** - Standing around without walking.
+- **`9|other`** - Any other activity, including e.g. walking while pushing an object like a chair.
+### Extended Classes (10-15, OF-ItW and OF-Syn)
+These classes capture additional activities observed in genuine accident and synthetic videos. They are infrequent and do not occur in staged datasets.
+- **`10|kneel_down`** - Transitioning from standing or another posture to a kneeling position.
+- **`11|kneeling`** - Being in a kneeling position (static posture).
+- **`12|squat_down`** - Transitioning to a squatting position.
+- **`13|squatting`** - Being in a squatting position (static posture).
+- **`14|crawl`** - Crawling on hands and knees or similar locomotion on the ground.
+- **`15|jump`** - Jumping action, including vertical jumps and jumps from elevated positions.
+## Motion Types
+There are two types of motions, **dynamic** ones like `walk` or `stand_up` and **static** ones like `fallen`, `sitting`, `lying`.
+**Generally we annotate dynamic motions as soon as the first frame appears which belongs to that action**.
+Let's say we see a person `walk`, then `fall`. The first frame which indicates a motion which does seem to be different to `walk` is the start of `fall`. Sometimes it is necessary to have a look at a person walking to learn when the motion begins to change to something else.
+**For static motions the label begins with the first frame where the person comes to a resting state.** For `sit_down`, the label ends when the person reaches a state where it is no longer adjusting its body position but comes to a rest. `fall` ends when the person is no longer moving caused by the inertia of the fall. The following `fallen` might contain movement on the ground, but no movement which belongs to `fall` or `stand_up`.
+## Label Sequences
+There are some natural sequences of labels like `fall`, `fallen` and `stand_up`. However, it is not always the case that these appear together. Sometimes the person might directly stand up again without any time at rest on the ground, in this case there is no `fallen` segment. Likewise sometimes there is no sitting segment after sitting down (e.g. if the person immediately stands up again).
+Lying down can be on a bed or on the ground, it is intentional in contrast to `fall`. There are falls which follow `sit_down` or `lie_down` if it is from a chair or from a bed.
+When a person is lying in a bed and getting up to sit in the bed we label this as `stand_up`, even if the person is still sitting in the bed. A sequence could then be `lying`, `stand_up`, `sitting`, `stand_up` to describe a person which first lies down, then gets into a sitting position, waits a little, then gets fully up.
+Sometimes it is not perfectly clear if it is `sit_down` followed by `lie_down` or simply `lie_down`. This depends on there being a moment of rest or not. If the person spends a short amount of time in the sitting position it is the former, if the person directly goes from `standing` over `sit_down` to `lie_down` without rest it is only labeled `lie_down`. Similar thoughts apply to `stand_up`.
+In the video at the bottom of this page we show how we leveraged VGG VIA to perform the annotations. Note, that we pre-load the original dataset labels as visual aid, but relabel the whole video with our label definitions. In the video below, CMDFall is shown, which already provides relatively detailed labels, the original labels of other datasets are more sparse. Additionally, even CMDFall does not label all frames, but only specific segments, while our labels cover mostly every frame.
+The blurred out regions were added by us in post-processing to protect the subjects privacy on this page, they are not part of the original videos.
+## Citation
+If you use OmniFall in your research, please cite our paper (will be updated soon) as well as all sub-dataset papers:
+```bibtex
+@misc{omnifall,
+      title={OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection},
+      author={David Schneider and Zdravko Marinov and Rafael Baur and Zeyun Zhong and Rodi Düger and Rainer Stiefelhagen},
+      year={2025},
+      eprint={2505.19889},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.19889},
+},
+@inproceedings{omnifall_cmdfall,
+  title={A multi-modal multi-view dataset for human fall analysis and preliminary investigation on modality},
+  author={Tran, Thanh-Hai and Le, Thi-Lan and Pham, Dinh-Tan and Hoang, Van-Nam and Khong, Van-Minh and Tran, Quoc-Toan and Nguyen, Thai-Son and Pham, Cuong},
+  booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
+  pages={1947--1952},
+  year={2018},
+  organization={IEEE}
+},
+@article{omnifall_up-fall,
+  title={UP-fall detection dataset: A multimodal approach},
+  author={Mart{\'\i}nez-Villase{\~n}or, Lourdes and Ponce, Hiram and Brieva, Jorge and Moya-Albor, Ernesto and N{\'u}{\~n}ez-Mart{\'\i}nez, Jos{\'e} and Pe{\~n}afort-Asturiano, Carlos},
+  journal={Sensors},
+  volume={19},
+  number={9},
+  pages={1988},
+  year={2019},
+  publisher={MDPI}
+},
+@article{omnifall_le2i,
+  title={Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification},
+  author={Charfi, Imen and Miteran, Johel and Dubois, Julien and Atri, Mohamed and Tourki, Rached},
+  journal={Journal of Electronic Imaging},
+  volume={22},
+  number={4},
+  pages={041106--041106},
+  year={2013},
+  publisher={Society of Photo-Optical Instrumentation Engineers}
+},
+@article{omnifall_gmdcsa,
+  title={GMDCSA-24: A dataset for human fall detection in videos},
+  author={Alam, Ekram and Sufian, Abu and Dutta, Paramartha and Leo, Marco and Hameed, Ibrahim A},
+  journal={Data in Brief},
+  volume={57},
+  pages={110892},
+  year={2024},
+  publisher={Elsevier}
+},
+@article{omnifall_cauca,
+  title={Dataset CAUCAFall},
+  author={Eraso, Jose Camilo and Mu{\~n}oz, Elena and Mu{\~n}oz, Mariela and Pinto, Jesus},
+  journal={Mendeley Data},
+  volume={4},
+  year={2022}
+},
+@inproceedings{omnifall_edf_occu,
+  title={Evaluating depth-based computer vision methods for fall detection under occlusions},
+  author={Zhang, Zhong and Conly, Christopher and Athitsos, Vassilis},
+  booktitle={International symposium on visual computing},
+  pages={196--207},
+  year={2014},
+  organization={Springer}
+},
+@article{omnifall_mcfd,
+  title={Multiple cameras fall dataset},
+  author={Auvinet, Edouard and Rougier, Caroline and Meunier, Jean and St-Arnaud, Alain and Rousseau, Jacqueline},
+  journal={DIRO-Universit{\'e} de Montr{\'e}al, Tech. Rep},
+  volume={1350},
+  pages={24},
+  year={2010}
+},
+@inproceedings{omnifall_oops,
+  title={Oops! predicting unintentional action in video},
+  author={Epstein, Dave and Chen, Boyuan and Vondrick, Carl},
+  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
+  pages={919--929},
+  year={2020}
+}
 ```
+## License
+The annotations and split definitions in this repository are released under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+The original video data belongs to their respective owners and should be obtained from the original sources.
+## Contact
+For questions about the dataset, please contact [david.schneider@kit.edu].
+## How we used VGG VIA to densely annotate videos for Omnifall
+We provide this video to shortly demonstrate how the annotation process was conducted, increasing transparency. Note, that the CMDFall dataset already provides realtively detailed labels, this is not the case for many of the other datasets.
+<video src="https://huggingface.co/datasets/simplexsigil2/omnifall/resolve/main/annotation_remarks.webm" controls />

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labels/OOPS.csv ADDED Viewed

The diff for this file is too large to render. See raw diff

labels/caucafall.csv ADDED Viewed

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labels/cmdfall.csv ADDED Viewed

The diff for this file is too large to render. See raw diff

labels/edf.csv ADDED Viewed

	@@ -0,0 +1,509 @@

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	@@ -0,0 +1,485 @@

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omnifall.py ADDED Viewed

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+"""OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection
+This dataset builder provides unified access to the OmniFall benchmark, which integrates:
+- OF-Staged (OF-Sta): 8 public staged fall detection datasets (~14h single-view)
+- OF-In-the-Wild (OF-ItW): Curated genuine accident videos from OOPS (~2.7h)
+- OF-Synthetic (OF-Syn): 12,000 synthetic videos generated with Wan 2.2 (~17h)
+All components share a 16-class activity taxonomy. Staged datasets use classes 0-9,
+while OF-ItW and OF-Syn use the full 0-15 range.
+"""
+import warnings
+import pandas as pd
+import datasets
+from datasets import (
+    BuilderConfig,
+    GeneratorBasedBuilder,
+    Features,
+    Value,
+    ClassLabel,
+    Sequence,
+    SplitGenerator,
+    Split,
+    Video,
+)
+_CITATION = """\
+@misc{omnifall,
+      title={OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection},
+      author={David Schneider and Zdravko Marinov and Rafael Baur and Zeyun Zhong and Rodi D\\\"uger and Rainer Stiefelhagen},
+      year={2025},
+      eprint={2505.19889},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.19889},
+}
+"""
+_DESCRIPTION = """\
+OmniFall is a comprehensive benchmark that unifies staged, in-the-wild, and synthetic
+fall detection datasets under a common 16-class activity taxonomy.
+"""
+_HOMEPAGE = "https://huggingface.co/datasets/simplexsigil2/omnifall"
+_LICENSE = "cc-by-nc-4.0"
+# 16 activity classes shared across all components
+_ACTIVITY_LABELS = [
+    "walk",         # 0
+    "fall",         # 1
+    "fallen",       # 2
+    "sit_down",     # 3
+    "sitting",      # 4
+    "lie_down",     # 5
+    "lying",        # 6
+    "stand_up",     # 7
+    "standing",     # 8
+    "other",        # 9
+    "kneel_down",   # 10
+    "kneeling",     # 11
+    "squat_down",   # 12
+    "squatting",    # 13
+    "crawl",        # 14
+    "jump",         # 15
+]
+# Demographic and scene metadata categories (OF-Syn only)
+_AGE_GROUPS = [
+    "toddlers_1_4", "children_5_12", "teenagers_13_17",
+    "young_adults_18_34", "middle_aged_35_64", "elderly_65_plus",
+]
+_GENDERS = ["male", "female"]
+_SKIN_TONES = [f"mst{i}" for i in range(1, 11)]
+_ETHNICITIES = ["white", "black", "asian", "hispanic_latino", "aian", "nhpi", "mena"]
+_BMI_BANDS = ["underweight", "normal", "overweight", "obese"]
+_HEIGHT_BANDS = ["short", "avg", "tall"]
+_ENVIRONMENTS = ["indoor", "outdoor"]
+_CAMERA_ELEVATIONS = ["eye", "low", "high", "top"]
+_CAMERA_AZIMUTHS = ["front", "rear", "left", "right"]
+_CAMERA_DISTANCES = ["medium", "far"]
+_CAMERA_SHOTS = ["static_wide", "static_medium_wide"]
+_SPEEDS = ["24fps_rt", "25fps_rt", "30fps_rt", "std_rt"]
+# The 8 staged datasets
+_STAGED_DATASETS = [
+    "caucafall", "cmdfall", "edf", "gmdcsa24",
+    "le2i", "mcfd", "occu", "up_fall",
+]
+# Label CSV file paths (relative to repo root)
+_STAGED_LABEL_FILES = [f"labels/{name}.csv" for name in [
+    "caucafall", "cmdfall", "edf", "GMDCSA24",
+    "le2i", "mcfd", "occu", "up_fall",
+]]
+_ITW_LABEL_FILE = "labels/OOPS.csv"
+_SYN_LABEL_FILE = "labels/of-syn.csv"
+_SYN_VIDEO_ARCHIVE = "data_files/omnifall-synthetic_av1.tar"
+# ---- Feature schema definitions ----
+def _core_features():
+    """7-column schema for staged/OOPS data."""
+    return Features({
+        "path": Value("string"),
+        "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS),
+        "start": Value("float32"),
+        "end": Value("float32"),
+        "subject": Value("int32"),
+        "cam": Value("int32"),
+        "dataset": Value("string"),
+    })
+def _syn_features():
+    """19-column schema for synthetic data (core + demographic/scene metadata)."""
+    return Features({
+        "path": Value("string"),
+        "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS),
+        "start": Value("float32"),
+        "end": Value("float32"),
+        "subject": Value("int32"),
+        "cam": Value("int32"),
+        "dataset": Value("string"),
+        # Demographic metadata
+        "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+        "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+        "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+        "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+        "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+        "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+        # Scene metadata
+        "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+        "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+        "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+        "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+        "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+        "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+    })
+def _syn_metadata_features():
+    """Feature schema for OF-Syn metadata config (video-level, no temporal segments)."""
+    return Features({
+        "path": Value("string"),
+        "dataset": Value("string"),
+        "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+        "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+        "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+        "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+        "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+        "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+        "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+        "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+        "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+        "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+        "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+        "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+    })
+def _syn_framewise_features():
+    """Feature schema for OF-Syn frame-wise labels (81 labels per video)."""
+    return Features({
+        "path": Value("string"),
+        "dataset": Value("string"),
+        "frame_labels": Sequence(
+            ClassLabel(num_classes=16, names=_ACTIVITY_LABELS), length=81
+        ),
+        "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+        "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+        "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+        "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+        "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+        "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+        "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+        "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+        "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+        "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+        "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+        "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+    })
+def _paths_only_features():
+    """Minimal feature schema for paths-only mode."""
+    return Features({"path": Value("string")})
+# ---- Config ----
+class OmniFallConfig(BuilderConfig):
+    """BuilderConfig for OmniFall dataset.
+    Args:
+        config_type: What kind of data to load.
+            "labels" - All labels in a single split (no train/val/test).
+            "split" - Train/val/test splits from split CSV files.
+            "metadata" - Video-level metadata (OF-Syn only).
+            "framewise" - Frame-wise HDF5 labels (OF-Syn only).
+        data_source: Which component(s) to load.
+            "staged" - 8 staged lab datasets
+            "itw" - OOPS in-the-wild
+            "syn" - OF-Syn synthetic
+            "staged+itw" - Staged and OOPS combined
+            Individual dataset names (e.g. "cmdfall") for single datasets.
+        split_type: Split strategy.
+            "cs" / "cv" for staged/OOPS, "random" / "cross_age" / etc. for synthetic.
+        train_source: For cross-domain configs, overrides data_source for train/val.
+        test_source: For cross-domain configs, overrides data_source for test.
+        test_split_type: For cross-domain configs, overrides split_type for test.
+        paths_only: If True, only return video paths (no label merging).
+        framewise: If True, load frame-wise labels from HDF5 (OF-Syn only).
+        include_video: If True, download and include video files (OF-Syn only).
+        decode_video: If True (default), use Video() feature for auto-decoding.
+            If False, return absolute file path as string.
+        deprecated_alias_for: If set, this config is a deprecated alias.
+    """
+    def __init__(
+        self,
+        config_type="labels",
+        data_source="staged+itw",
+        split_type=None,
+        train_source=None,
+        test_source=None,
+        test_split_type=None,
+        paths_only=False,
+        framewise=False,
+        include_video=False,
+        decode_video=True,
+        deprecated_alias_for=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.config_type = config_type
+        self.data_source = data_source
+        self.split_type = split_type
+        self.train_source = train_source
+        self.test_source = test_source
+        self.test_split_type = test_split_type
+        self.paths_only = paths_only
+        self.framewise = framewise
+        self.include_video = include_video
+        self.decode_video = decode_video
+        self.deprecated_alias_for = deprecated_alias_for
+    @property
+    def is_crossdomain(self):
+        return self.train_source is not None
+def _make_config(name, description, **kwargs):
+    """Helper to create a config with consistent version."""
+    return OmniFallConfig(
+        name=name,
+        version=datasets.Version("2.0.0"),
+        description=description,
+        **kwargs,
+    )
+# ---- Config definitions ----
+_LABELS_CONFIGS = [
+    _make_config(
+        "labels",
+        "All staged + OOPS labels (52k segments, 7 columns). Default config.",
+        config_type="labels",
+        data_source="staged+itw",
+    ),
+    _make_config(
+        "labels-syn",
+        "OF-Syn labels with demographic metadata (19k segments, 19 columns).",
+        config_type="labels",
+        data_source="syn",
+    ),
+    _make_config(
+        "metadata-syn",
+        "OF-Syn video-level metadata (12k videos, no temporal segments).",
+        config_type="metadata",
+        data_source="syn",
+    ),
+    _make_config(
+        "framewise-syn",
+        "OF-Syn frame-wise labels from HDF5 (81 labels per video).",
+        config_type="framewise",
+        data_source="syn",
+        framewise=True,
+    ),
+]
+_AGGREGATE_CONFIGS = [
+    _make_config(
+        "cs",
+        "Cross-subject splits for all staged + OOPS datasets combined.",
+        config_type="split",
+        data_source="staged+itw",
+        split_type="cs",
+    ),
+    _make_config(
+        "cv",
+        "Cross-view splits for all staged + OOPS datasets combined.",
+        config_type="split",
+        data_source="staged+itw",
+        split_type="cv",
+    ),
+]
+_PRIMARY_CONFIGS = [
+    _make_config(
+        "of-sta-cs",
+        "OF-Staged: 8 staged datasets, cross-subject splits.",
+        config_type="split",
+        data_source="staged",
+        split_type="cs",
+    ),
+    _make_config(
+        "of-sta-cv",
+        "OF-Staged: 8 staged datasets, cross-view splits.",
+        config_type="split",
+        data_source="staged",
+        split_type="cv",
+    ),
+    _make_config(
+        "of-itw",
+        "OF-ItW: OOPS-Fall in-the-wild genuine accidents.",
+        config_type="split",
+        data_source="itw",
+        split_type="cs",
+    ),
+    _make_config(
+        "of-syn",
+        "OF-Syn: synthetic, random 80/10/10 split.",
+        config_type="split",
+        data_source="syn",
+        split_type="random",
+    ),
+    _make_config(
+        "of-syn-cross-age",
+        "OF-Syn: cross-age split (train: adults, test: children/elderly).",
+        config_type="split",
+        data_source="syn",
+        split_type="cross_age",
+    ),
+    _make_config(
+        "of-syn-cross-ethnicity",
+        "OF-Syn: cross-ethnicity split.",
+        config_type="split",
+        data_source="syn",
+        split_type="cross_ethnicity",
+    ),
+    _make_config(
+        "of-syn-cross-bmi",
+        "OF-Syn: cross-BMI split (train: normal/underweight, test: obese).",
+        config_type="split",
+        data_source="syn",
+        split_type="cross_bmi",
+    ),
+]
+_CROSSDOMAIN_CONFIGS = [
+    _make_config(
+        "of-sta-itw-cs",
+        "Cross-domain: train/val on staged CS, test on OOPS.",
+        config_type="split",
+        data_source="staged",
+        split_type="cs",
+        train_source="staged",
+        test_source="itw",
+        test_split_type="cs",
+    ),
+    _make_config(
+        "of-sta-itw-cv",
+        "Cross-domain: train/val on staged CV, test on OOPS.",
+        config_type="split",
+        data_source="staged",
+        split_type="cv",
+        train_source="staged",
+        test_source="itw",
+        test_split_type="cv",
+    ),
+    _make_config(
+        "of-syn-itw",
+        "Cross-domain: train/val on OF-Syn random, test on OOPS.",
+        config_type="split",
+        data_source="syn",
+        split_type="random",
+        train_source="syn",
+        test_source="itw",
+        test_split_type="cs",
+    ),
+]
+_INDIVIDUAL_CONFIGS = [
+    _make_config(
+        name,
+        f"{name} dataset with cross-subject splits.",
+        config_type="split",
+        data_source=name,
+        split_type="cs",
+    )
+    for name in _STAGED_DATASETS
+]
+# Deprecated aliases: defined with full correct attributes so _info() works
+# immediately (HF calls _info() during __init__, before any custom init code).
+_DEPRECATED_ALIASES = {
+    "cs-staged": "of-sta-cs",
+    "cv-staged": "of-sta-cv",
+    "cs-staged-wild": "of-sta-itw-cs",
+    "cv-staged-wild": "of-sta-itw-cv",
+    "OOPS": "of-itw",
+}
+# Build a lookup from config name to config object
+_ALL_NAMED_CONFIGS = {
+    cfg.name: cfg
+    for cfg in (
+        _LABELS_CONFIGS + _AGGREGATE_CONFIGS + _PRIMARY_CONFIGS
+        + _CROSSDOMAIN_CONFIGS + _INDIVIDUAL_CONFIGS
+    )
+}
+_DEPRECATED_CONFIGS = []
+for _old_name, _new_name in _DEPRECATED_ALIASES.items():
+    _target = _ALL_NAMED_CONFIGS[_new_name]
+    _DEPRECATED_CONFIGS.append(
+        _make_config(
+            _old_name,
+            f"DEPRECATED: Use '{_new_name}' instead.",
+            config_type=_target.config_type,
+            data_source=_target.data_source,
+            split_type=_target.split_type,
+            train_source=_target.train_source,
+            test_source=_target.test_source,
+            test_split_type=_target.test_split_type,
+            paths_only=_target.paths_only,
+            framewise=_target.framewise,
+            include_video=_target.include_video,
+            decode_video=_target.decode_video,
+            deprecated_alias_for=_new_name,
+        )
+    )
+# ---- Builder ----
+class OmniFall(GeneratorBasedBuilder):
+    """OmniFall unified fall detection benchmark builder."""
+    VERSION = datasets.Version("2.0.0")
+    BUILDER_CONFIG_CLASS = OmniFallConfig
+    BUILDER_CONFIGS = (
+        _LABELS_CONFIGS
+        + _AGGREGATE_CONFIGS
+        + _PRIMARY_CONFIGS
+        + _CROSSDOMAIN_CONFIGS
+        + _INDIVIDUAL_CONFIGS
+        + _DEPRECATED_CONFIGS
+    )
+    DEFAULT_CONFIG_NAME = "labels"
+    def _info(self):
+        """Return dataset metadata and feature schema."""
+        cfg = self.config
+        if cfg.config_type == "metadata":
+            features = _syn_metadata_features()
+        elif cfg.framewise:
+            features = _syn_framewise_features()
+        elif cfg.paths_only:
+            features = _paths_only_features()
+        elif cfg.is_crossdomain:
+            # Cross-domain configs mix sources, use common 7-col schema
+            features = _core_features()
+        elif cfg.data_source == "syn":
+            features = _syn_features()
+        else:
+            features = _core_features()
+        if cfg.include_video:
+            features["video"] = Video() if cfg.decode_video else Value("string")
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    # ---- Split generators ----
+    def _split_generators(self, dl_manager):
+        cfg = self.config
+        # Emit deprecation warning
+        if cfg.deprecated_alias_for:
+            warnings.warn(
+                f"Config '{cfg.name}' is deprecated. "
+                f"Use '{cfg.deprecated_alias_for}' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        # Labels configs: all data in a single "train" split
+        if cfg.config_type == "labels":
+            return self._labels_splits(cfg, dl_manager)
+        # Metadata config
+        if cfg.config_type == "metadata":
+            metadata_path = dl_manager.download("videos/metadata.csv")
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "metadata", "metadata_path": metadata_path},
+                ),
+            ]
+        # Framewise config (no split, all data)
+        if cfg.config_type == "framewise":
+            archive_path = dl_manager.download_and_extract(
+                "data_files/syn_frame_wise_labels.tar.zst"
+            )
+            metadata_path = dl_manager.download("videos/metadata.csv")
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={
+                        "mode": "framewise",
+                        "hdf5_dir": archive_path,
+                        "metadata_path": metadata_path,
+                        "split_file": None,
+                    },
+                ),
+            ]
+        # Split configs (train/val/test)
+        if cfg.config_type == "split":
+            return self._split_config_generators(cfg, dl_manager)
+        raise ValueError(f"Unknown config_type: {cfg.config_type}")
+    def _labels_splits(self, cfg, dl_manager):
+        """Generate split generators for labels-type configs."""
+        if cfg.data_source == "syn":
+            filepath = dl_manager.download(_SYN_LABEL_FILE)
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "csv_direct", "filepath": filepath},
+                ),
+            ]
+        elif cfg.data_source == "staged+itw":
+            filepaths = dl_manager.download(_STAGED_LABEL_FILES + [_ITW_LABEL_FILE])
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "csv_multi", "filepaths": filepaths},
+                ),
+            ]
+        else:
+            raise ValueError(f"Unsupported data_source for labels: {cfg.data_source}")
+    def _split_config_generators(self, cfg, dl_manager):
+        """Generate split generators for train/val/test split configs."""
+        if cfg.is_crossdomain:
+            return self._crossdomain_splits(cfg, dl_manager)
+        if cfg.data_source == "syn":
+            return self._syn_splits(cfg, dl_manager)
+        elif cfg.data_source == "staged":
+            return self._staged_splits(cfg, dl_manager)
+        elif cfg.data_source == "itw":
+            return self._itw_splits(cfg, dl_manager)
+        elif cfg.data_source == "staged+itw":
+            return self._aggregate_splits(cfg, dl_manager)
+        elif cfg.data_source in _STAGED_DATASETS:
+            return self._individual_splits(cfg, dl_manager)
+        else:
+            raise ValueError(f"Unknown data_source: {cfg.data_source}")
+    def _staged_split_files(self, split_type, split_name):
+        """Return list of split CSV paths for all 8 staged datasets."""
+        return [f"splits/{split_type}/{ds}/{split_name}.csv" for ds in _STAGED_DATASETS]
+    def _make_split_merge_generators(self, split_files_per_split, label_files,
+                                     dl_manager, video_dir=None):
+        """Helper to create train/val/test SplitGenerators for split_merge mode.
+        Args:
+            split_files_per_split: dict mapping split name to list of relative paths.
+            label_files: list of relative label file paths.
+            dl_manager: download manager for resolving paths.
+            video_dir: path to extracted video directory, or None.
+        """
+        resolved_labels = dl_manager.download(label_files)
+        return [
+            SplitGenerator(
+                name=split_enum,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": dl_manager.download(split_files_per_split[csv_name]),
+                    "label_files": resolved_labels,
+                    "video_dir": video_dir,
+                },
+            )
+            for split_enum, csv_name in [
+                (Split.TRAIN, "train"),
+                (Split.VALIDATION, "val"),
+                (Split.TEST, "test"),
+            ]
+        ]
+    def _staged_splits(self, cfg, dl_manager):
+        """OF-Staged: 8 datasets combined with CS or CV splits."""
+        st = cfg.split_type
+        return self._make_split_merge_generators(
+            {sn: self._staged_split_files(st, sn) for sn in ("train", "val", "test")},
+            _STAGED_LABEL_FILES,
+            dl_manager,
+        )
+    def _itw_splits(self, cfg, dl_manager):
+        """OF-ItW: OOPS-Fall (CS=CV identical)."""
+        st = cfg.split_type
+        return self._make_split_merge_generators(
+            {sn: [f"splits/{st}/OOPS/{sn}.csv"] for sn in ("train", "val", "test")},
+            [_ITW_LABEL_FILE],
+            dl_manager,
+        )
+    def _aggregate_splits(self, cfg, dl_manager):
+        """All staged + OOPS combined (cs or cv)."""
+        st = cfg.split_type
+        all_labels = _STAGED_LABEL_FILES + [_ITW_LABEL_FILE]
+        return self._make_split_merge_generators(
+            {sn: self._staged_split_files(st, sn) + [f"splits/{st}/OOPS/{sn}.csv"]
+             for sn in ("train", "val", "test")},
+            all_labels,
+            dl_manager,
+        )
+    def _individual_splits(self, cfg, dl_manager):
+        """Individual dataset with CS splits."""
+        ds_name = cfg.data_source
+        label_file_map = {
+            "caucafall": "labels/caucafall.csv",
+            "cmdfall": "labels/cmdfall.csv",
+            "edf": "labels/edf.csv",
+            "gmdcsa24": "labels/GMDCSA24.csv",
+            "le2i": "labels/le2i.csv",
+            "mcfd": "labels/mcfd.csv",
+            "occu": "labels/occu.csv",
+            "up_fall": "labels/up_fall.csv",
+        }
+        label_file = label_file_map[ds_name]
+        st = cfg.split_type
+        return self._make_split_merge_generators(
+            {sn: [f"splits/{st}/{ds_name}/{sn}.csv"] for sn in ("train", "val", "test")},
+            [label_file],
+            dl_manager,
+        )
+    def _syn_splits(self, cfg, dl_manager):
+        """OF-Syn split strategies."""
+        st = cfg.split_type
+        split_dir = f"splits/syn/{st}"
+        # Download video archive if requested
+        video_dir = None
+        if cfg.include_video:
+            video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE)
+        if cfg.framewise:
+            archive_path = dl_manager.download_and_extract(
+                "data_files/syn_frame_wise_labels.tar.zst"
+            )
+            metadata_path = dl_manager.download("videos/metadata.csv")
+            split_files = dl_manager.download(
+                {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")}
+            )
+            return [
+                SplitGenerator(
+                    name=split_enum,
+                    gen_kwargs={
+                        "mode": "framewise",
+                        "hdf5_dir": archive_path,
+                        "metadata_path": metadata_path,
+                        "split_file": split_files[csv_name],
+                    },
+                )
+                for split_enum, csv_name in [
+                    (Split.TRAIN, "train"),
+                    (Split.VALIDATION, "val"),
+                    (Split.TEST, "test"),
+                ]
+            ]
+        if cfg.paths_only:
+            split_files = dl_manager.download(
+                {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")}
+            )
+            return [
+                SplitGenerator(
+                    name=split_enum,
+                    gen_kwargs={
+                        "mode": "paths_only",
+                        "split_file": split_files[csv_name],
+                    },
+                )
+                for split_enum, csv_name in [
+                    (Split.TRAIN, "train"),
+                    (Split.VALIDATION, "val"),
+                    (Split.TEST, "test"),
+                ]
+            ]
+        return self._make_split_merge_generators(
+            {sn: [f"{split_dir}/{sn}.csv"] for sn in ("train", "val", "test")},
+            [_SYN_LABEL_FILE],
+            dl_manager,
+            video_dir=video_dir,
+        )
+    def _crossdomain_splits(self, cfg, dl_manager):
+        """Cross-domain configs: train/val from one source, test from another."""
+        train_st = cfg.split_type
+        test_st = cfg.test_split_type or "cs"
+        # Download video archive if requested and train source is syn
+        video_dir = None
+        if cfg.include_video and cfg.train_source == "syn":
+            video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE)
+        # Determine train/val files and labels
+        if cfg.train_source == "staged":
+            train_split_files = {
+                sn: self._staged_split_files(train_st, sn)
+                for sn in ("train", "val")
+            }
+            train_labels = _STAGED_LABEL_FILES
+        elif cfg.train_source == "syn":
+            train_split_files = {
+                sn: [f"splits/syn/{train_st}/{sn}.csv"]
+                for sn in ("train", "val")
+            }
+            train_labels = [_SYN_LABEL_FILE]
+        else:
+            raise ValueError(f"Unsupported train_source: {cfg.train_source}")
+        # Determine test files and labels
+        if cfg.test_source == "itw":
+            test_split_files = [f"splits/{test_st}/OOPS/test.csv"]
+            test_labels = [_ITW_LABEL_FILE]
+        else:
+            raise ValueError(f"Unsupported test_source: {cfg.test_source}")
+        # Download all paths
+        resolved_train_labels = dl_manager.download(train_labels)
+        resolved_test_labels = dl_manager.download(test_labels)
+        resolved_test_splits = dl_manager.download(test_split_files)
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": dl_manager.download(train_split_files["train"]),
+                    "label_files": resolved_train_labels,
+                    "video_dir": video_dir,
+                },
+            ),
+            SplitGenerator(
+                name=Split.VALIDATION,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": dl_manager.download(train_split_files["val"]),
+                    "label_files": resolved_train_labels,
+                    "video_dir": video_dir,
+                },
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": resolved_test_splits,
+                    "label_files": resolved_test_labels,
+                    "video_dir": None,  # test source (itw) has no hosted videos
+                },
+            ),
+        ]
+    # ---- Example generators ----
+    def _generate_examples(self, mode, **kwargs):
+        """Dispatch to the appropriate generator based on mode."""
+        if mode == "csv_direct":
+            yield from self._gen_csv_direct(**kwargs)
+        elif mode == "csv_multi":
+            yield from self._gen_csv_multi(**kwargs)
+        elif mode == "split_merge":
+            yield from self._gen_split_merge(**kwargs)
+        elif mode == "metadata":
+            yield from self._gen_metadata(**kwargs)
+        elif mode == "framewise":
+            yield from self._gen_framewise(**kwargs)
+        elif mode == "paths_only":
+            yield from self._gen_paths_only(**kwargs)
+        else:
+            raise ValueError(f"Unknown generation mode: {mode}")
+    def _gen_csv_direct(self, filepath):
+        """Load a single CSV file directly."""
+        df = pd.read_csv(filepath)
+        for idx, row in df.iterrows():
+            yield idx, self._row_to_example(row)
+    def _gen_csv_multi(self, filepaths):
+        """Load and concatenate multiple CSV files."""
+        dfs = [pd.read_csv(fp) for fp in filepaths]
+        df = pd.concat(dfs, ignore_index=True)
+        for idx, row in df.iterrows():
+            yield idx, self._row_to_example(row)
+    def _gen_split_merge(self, split_files, label_files, video_dir=None):
+        """Load split paths, merge with labels, yield examples."""
+        import os
+        split_dfs = [pd.read_csv(sf) for sf in split_files]
+        split_df = pd.concat(split_dfs, ignore_index=True)
+        if self.config.paths_only:
+            for idx, row in split_df.iterrows():
+                yield idx, {"path": row["path"]}
+            return
+        label_dfs = [pd.read_csv(lf) for lf in label_files]
+        labels_df = pd.concat(label_dfs, ignore_index=True)
+        merged_df = pd.merge(split_df, labels_df, on="path", how="left")
+        for idx, row in merged_df.iterrows():
+            example = self._row_to_example(row)
+            if video_dir is not None:
+                example["video"] = os.path.join(video_dir, row["path"] + ".mp4")
+            yield idx, example
+    def _gen_metadata(self, metadata_path):
+        """Load OF-Syn video-level metadata."""
+        df = pd.read_csv(metadata_path)
+        metadata_cols = [
+            "path", "age_group", "gender_presentation", "monk_skin_tone",
+            "race_ethnicity_omb", "bmi_band", "height_band",
+            "environment_category", "camera_shot", "speed",
+            "camera_elevation", "camera_azimuth", "camera_distance",
+        ]
+        available_cols = [c for c in metadata_cols if c in df.columns]
+        df = df[available_cols].drop_duplicates(subset=["path"]).reset_index(drop=True)
+        df["dataset"] = "of-syn"
+        for idx, row in df.iterrows():
+            yield idx, self._row_to_example(row)
+    def _gen_framewise(self, hdf5_dir, metadata_path, split_file=None):
+        """Load frame-wise labels from HDF5 files with metadata."""
+        import h5py
+        import tarfile
+        from pathlib import Path
+        metadata_df = pd.read_csv(metadata_path)
+        valid_paths = None
+        if split_file is not None:
+            split_df = pd.read_csv(split_file)
+            valid_paths = set(split_df["path"].tolist())
+        hdf5_path = Path(hdf5_dir)
+        metadata_fields = [
+            "age_group", "gender_presentation", "monk_skin_tone",
+            "race_ethnicity_omb", "bmi_band", "height_band",
+            "environment_category", "camera_shot", "speed",
+            "camera_elevation", "camera_azimuth", "camera_distance",
+        ]
+        if hdf5_path.is_file() and (
+            hdf5_path.suffix == ".tar" or tarfile.is_tarfile(str(hdf5_path))
+        ):
+            idx = 0
+            with tarfile.open(hdf5_path, "r") as tar:
+                for member in tar.getmembers():
+                    if not member.name.endswith(".h5"):
+                        continue
+                    video_path = member.name.lstrip("./").replace(".h5", "")
+                    if valid_paths is not None and video_path not in valid_paths:
+                        continue
+                    try:
+                        h5_file = tar.extractfile(member)
+                        if h5_file is None:
+                            continue
+                        import tempfile
+                        with tempfile.NamedTemporaryFile(suffix=".h5", delete=True) as tmp:
+                            tmp.write(h5_file.read())
+                            tmp.flush()
+                            with h5py.File(tmp.name, "r") as f:
+                                frame_labels = f["label_indices"][:].tolist()
+                        video_metadata = metadata_df[metadata_df["path"] == video_path]
+                        if len(video_metadata) == 0:
+                            continue
+                        video_meta = video_metadata.iloc[0]
+                        example = {
+                            "path": video_path,
+                            "dataset": "of-syn",
+                            "frame_labels": frame_labels,
+                        }
+                        for field in metadata_fields:
+                            if field in video_meta and pd.notna(video_meta[field]):
+                                example[field] = str(video_meta[field])
+                        yield idx, example
+                        idx += 1
+                    except Exception as e:
+                        warnings.warn(f"Failed to process {member.name}: {e}")
+                        continue
+        else:
+            hdf5_files = sorted(hdf5_path.glob("**/*.h5"))
+            idx = 0
+            for h5_file_path in hdf5_files:
+                relative_path = h5_file_path.relative_to(hdf5_path)
+                video_path = str(relative_path.with_suffix(""))
+                if valid_paths is not None and video_path not in valid_paths:
+                    continue
+                try:
+                    with h5py.File(h5_file_path, "r") as f:
+                        frame_labels = f["label_indices"][:].tolist()
+                    video_metadata = metadata_df[metadata_df["path"] == video_path]
+                    if len(video_metadata) == 0:
+                        continue
+                    video_meta = video_metadata.iloc[0]
+                    example = {
+                        "path": video_path,
+                        "dataset": "of-syn",
+                        "frame_labels": frame_labels,
+                    }
+                    for field in metadata_fields:
+                        if field in video_meta and pd.notna(video_meta[field]):
+                            example[field] = str(video_meta[field])
+                    yield idx, example
+                    idx += 1
+                except Exception as e:
+                    warnings.warn(f"Failed to process {h5_file_path}: {e}")
+                    continue
+    def _gen_paths_only(self, split_file):
+        """Load paths only from a split file."""
+        df = pd.read_csv(split_file)
+        for idx, row in df.iterrows():
+            yield idx, {"path": row["path"]}
+    def _row_to_example(self, row):
+        """Convert a DataFrame row to a typed example dict.
+        Only includes fields present in the row. HuggingFace's Features.encode_example()
+        will ignore extra fields and fill missing optional fields.
+        """
+        example = {"path": str(row["path"])}
+        # Core temporal fields
+        for field, dtype in [
+            ("label", int), ("start", float), ("end", float),
+            ("subject", int), ("cam", int),
+        ]:
+            if field in row.index and pd.notna(row[field]):
+                example[field] = dtype(row[field])
+        if "dataset" in row.index and pd.notna(row["dataset"]):
+            example["dataset"] = str(row["dataset"])
+        # Demographic and scene metadata (present only for syn data)
+        for field in [
+            "age_group", "gender_presentation", "monk_skin_tone",
+            "race_ethnicity_omb", "bmi_band", "height_band",
+            "environment_category", "camera_shot", "speed",
+            "camera_elevation", "camera_azimuth", "camera_distance",
+        ]:
+            if field in row.index and pd.notna(row[field]):
+                example[field] = str(row[field])
+        return example

splits/cs/OOPS/test.csv ADDED Viewed

	@@ -0,0 +1,717 @@

+path
+falls/BestFailsofWeek1July2016_FailArmy8
+falls/BestFailsofWeek2July2016_FailArmy23
+falls/BestFailsofWeek2July2016_FailArmy34
+falls/BestFailsofWeek2June2016_FailArmy16
+falls/BestFailsofWeek2June2016_FailArmy34
+falls/BestFailsofWeek3June2016_FailArmy57
+falls/BestFailsofWeek4June2016_FailArmy25
+falls/BestFailsofWeek4June2016_FailArmy56
+falls/BestFailsofWeek4June2016_FailArmy56
+falls/BestFailsoftheMonth-DrivingMeCrazyJanuary2018_FailArmy14
+falls/BestFailsoftheMonth-DrivingMeCrazyJanuary2018_FailArmy17
+falls/BestFailsoftheMonth-DrivingMeCrazyJanuary2018_FailArmy17
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	@@ -0,0 +1,3 @@

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