---
license: cc-by-4.0
task_categories:
- video-classification
language:
- en
pretty_name: EatBench-2.7K
size_categories:
- 1K<n<10K
tags:
- eating-behavior
- temporal-action-localization
- temporal-grounding
- video-understanding
- action-recognition
- fine-grained
- eating-disorders
- health
---

# EatBench-2.7K: A Benchmark for Fine-Grained Eating Action Grounding in Videos

## Dataset Summary

**EatBench-2.7K** is the first video benchmark for fine-grained eating-action grounding. It contains **525 video clips** with **2,690 temporally annotated micro-action instances** spanning **10 food categories** and **3 eating stages**. The benchmark is designed to evaluate models on precise temporal localization of sub-second eating micro-actions, posing distinct challenges that current vision-language models (VLMs) are not designed for.

## Dataset Details

### Eating Action Taxonomy

Each video is annotated with a three-stage eating-action taxonomy:

| Label | Abbreviation | Description |
|---|---|---|
| Contacting Food | CF | Interaction between hand/utensil and food (reaching, grasping, scooping) |
| Food Approaching Mouth | FAM | Transporting food from hand/utensil up to the mouth |
| Food in Mouth | FIM | Ingestion phase after food reaches the mouth (chewing, swallowing) |

### Statistics

| Split | Videos | Action Instances |
|---|---|---|
| Test (full) | 525 | 2,690 |

**EatBench-2.7K is a test-only benchmark.** The entire dataset serves as the evaluation set for assessing pretrained and training-free methods without task-specific fine-tuning.

#### Per-Category Statistics

| Category | Videos | Instances | Actions/Video | Avg Video Duration (s) | Avg Action Duration (s) |
|---|---|---|---|---|---|
| Burger | 55 | 251 | 4.56 | 21.82 | 3.81 |
| Cake | 68 | 364 | 5.35 | 20.42 | 2.87 |
| Carrots | 49 | 237 | 4.84 | 21.40 | 3.28 |
| Chips | 58 | 297 | 5.12 | 20.74 | 3.15 |
| Doughnuts | 43 | 196 | 4.56 | 21.01 | 3.53 |
| Hotdog | 42 | 167 | 3.98 | 21.79 | 4.25 |
| Ice Cream | 64 | 453 | 7.08 | 19.15 | 1.84 |
| Nachos | 35 | 189 | 5.40 | 21.78 | 3.22 |
| Spaghetti | 72 | 319 | 4.43 | 20.25 | 3.59 |
| Watermelon | 39 | 217 | 5.56 | 21.72 | 3.09 |
| **All** | **525** | **2,690** | **5.12** | **20.86** | **3.11** |

#### Action Class Statistics

| Class | Count | % | Mean (s) | Median (s) | Min (s) | Max (s) |
|---|---|---|---|---|---|---|
| CF | 436 | 16.2% | 3.341 | 2.312 | 0.083 | 21.500 |
| FAM | 1,036 | 38.5% | 0.962 | 0.792 | 0.083 | 10.375 |
| FIM | 1,218 | 45.3% | 4.860 | 3.667 | 0.042 | 25.208 |
| **All** | **2,690** | — | 3.113 | 1.583 | 0.042 | 25.208 |

Key properties:
- **600× temporal scale variation** (0.042s to 25.2s)
- **FAM is sub-second** in 67.4% of instances (median 0.79s)
- **69.3% of videos begin mid-eating-cycle** (naturalistic recording)
- **Near-deterministic action chain**: CF→FAM (94%) →FIM (99%)

## Dataset Structure

```
EatBench-2.7K/
├── README.md
├── eatbench_annotation_full.json   # Full annotations for all 525 videos
└── videos/                         # 525 MP4 video clips
    ├── <video_id>_eating_<category>.mp4
    └── ...
```

### Annotation Format

`eatbench_annotation_full.json` is a JSON array of 525 entries, one per video:

```json
[
  {
    "Video Name": "Nhj0LHx5BZc_138665_26.50_51.50_eating_cake.mp4",
    "Category": "Cake",
    "Actions": [
      {
        "Label": "Contacting Food",
        "Start": 10.417,
        "End": 12.417,
        "Duration": 2.0,
        "Video_Duration": 26.0
      },
      {
        "Label": "Food Approaching Mouth",
        "Start": 12.417,
        "End": 12.917,
        "Duration": 0.5,
        "Video_Duration": 26.0
      },
      {
        "Label": "Food in Mouth",
        "Start": 12.917,
        "End": 13.583,
        "Duration": 0.667,
        "Video_Duration": 26.0
      }
    ]
  },
  ...
]
```

**Fields:**
- `Video Name` (str): filename of the video clip in `videos/`
- `Category` (str): food category (one of 10 categories above)
- `Actions` (list): temporally annotated micro-action instances
  - `Label` (str): one of `"Contacting Food"`, `"Food Approaching Mouth"`, `"Food in Mouth"`
  - `Start` (float): start timestamp in seconds
  - `End` (float): end timestamp in seconds
  - `Duration` (float): `End - Start` in seconds
  - `Video_Duration` (float): total duration of the video clip in seconds

## Evaluation Protocol

EatBench-2.7K is formulated as a **fine-grained temporal action localization** task. Given a video, a model predicts a set of temporal segments `{(start, end, class)}` for the three micro-action classes.

**Matching:** For each video and class independently, predictions are matched to ground truth via **Hungarian matching** with tIoU threshold τ. Segments below threshold τ are forbidden from matching.

**Metrics:** Per-class precision, recall, and F1; **macro-averaged F1** across CF/FAM/FIM; and matched mean tIoU (mIoU). Primary evaluation threshold: **tIoU = 0.1**; also reported at tIoU ∈ {0.3, 0.5}.

Evaluation is **score-free**: predictions are matched on temporal overlap and class label only, without confidence ranking.

## Usage

```python
import json

with open("eatbench_annotation_full.json") as f:
    dataset = json.load(f)

# Each entry
for entry in dataset:
    video_name = entry["Video Name"]
    category = entry["Category"]
    actions = entry["Actions"]  # list of {Label, Start, End, Duration, Video_Duration}
    video_path = f"videos/{video_name}"
```

## Source Data

Video clips are sourced from **[Kinetics-400](https://www.deepmind.com/open-source/kinetics)**, a large-scale public action-recognition benchmark of YouTube videos collected under a Creative Commons license. We retain eating-related action categories and provide new fine-grained temporal annotations (CF, FAM, FIM) created by expert annotators.

## Benchmark Results

State-of-the-art zero-shot VLMs evaluated on EatBench-2.7K (Macro-F1 at tIoU=0.1):

| Model | Frame Selection | CF F1 | FAM F1 | FIM F1 | Macro-F1 |
|---|---|---|---|---|---|
| VideoChat-Flash | Uniform | 27.6 | 17.0 | 37.5 | 27.4 |
| OneThinker | Uniform | 30.4 | 22.2 | 43.9 | 32.2 |
| OneThinker | **SAFR** | **31.5** | **24.3** | **44.1** | **33.3** |
| VAPO-Thinker-7B | Uniform | 18.8 | 8.1 | 24.8 | 17.3 |
| VAPO-Thinker-7B | **SAFR** | 19.4 | 12.1 | 31.1 | 20.9 |
| Qwen2.5-VL-7B | Uniform | 19.5 | 11.6 | 25.0 | 18.7 |
| Qwen2.5-VL-7B | **SAFR** | 20.9 | 12.6 | 26.4 | 20.0 |
| InternVL3-8B | Uniform | 14.9 | 17.4 | 28.4 | 20.2 |
| InternVL3-8B | **SAFR** | 14.1 | 19.7 | 32.4 | 22.1 |

All models suffer >74% relative performance drop from tIoU=0.1 to tIoU=0.5, highlighting EatBench-2.7K as an open challenge for fine-grained temporal grounding.

## License

The annotations are released under **CC BY 4.0**. The video clips are derived from Kinetics-400 and subject to their original Creative Commons license terms.