---
dataset_info:
  features:
  - name: video_path
    dtype: string
  - name: participant
    dtype: string
  - name: camera
    dtype: string
  - name: video
    dtype: string
  - name: labels
    list:
    - name: start
      dtype: int64
    - name: end
      dtype: int64
    - name: label
      dtype: string
  splits:
  - name: s1
    num_bytes: 80320
    num_examples: 284
  - name: s2
    num_bytes: 137069
    num_examples: 506
  - name: s3
    num_bytes: 130705
    num_examples: 532
  - name: s4
    num_bytes: 166758
    num_examples: 667
  download_size: 107741
  dataset_size: 514852
configs:
- config_name: default
  data_files:
  - split: s1
    path: data/s1-*
  - split: s2
    path: data/s2-*
  - split: s3
    path: data/s3-*
  - split: s4
    path: data/s4-*
---

# 🍳 Breakfast Actions Dataset (HF + WebDataset Ready)

This repository hosts the **Breakfast Actions** dataset metadata and videos, organized for modern deep learning workflows.  
It provides:

- 4 evaluation splits (`s1`, `s2`, `s3`, `s4`)  
- JSONL metadata describing each video, participant, camera, and frame-level action segments  
- Raw AVI videos stored directly on HuggingFace  
- Optional WebDataset shards for streaming training

---

## 📁 Folder Layout

```
Breakfast-Actions/
│
├── Converted_Data/
│     ├── metadata_s1.jsonl
│     ├── metadata_s2.jsonl
│     ├── metadata_s3.jsonl
│     └── metadata_s4.jsonl
│
├── Videos/
│     ├── P03/cam01/*.avi
│     ├── P03/cam02/*.avi
│     ├── P04/cam01/*.avi
│     └── ... (participants P03–P54, multiple cameras)
│
└── WebDataset_Shards/   (optional)
       ├── 000000.tar
       ├── 000001.tar
       └── ...
```

---

## 📝 JSONL Record Format

Each metadata line looks like:

```json
{
  "video_path": "Videos/P03/cam01/P03_coffee.avi",
  "participant": "P03",
  "camera": "cam01",
  "video": "P03_coffee",
  "labels": [
      {"start": 1, "end": 385, "label": "SIL"},
      {"start": 385, "end": 599, "label": "pour_oil"},
      ...
  ]
}
```

All video paths match the directory structure inside the HF repo.

---

## 🔹 Load Metadata Using HuggingFace Datasets

```python
from datasets import load_dataset

ds = load_dataset("json", data_files="metadata_s2.jsonl")["train"]

# Select all videos belonging to split s2
subset = ds
```

---

## 🔹 Load and Decode a Video

### Using Decord

```python
from decord import VideoReader
item = ds[0]

vr = VideoReader(item["video_path"])
frame0 = vr[0]   # first frame
```

### Using TorchVision

```python
from torchvision.io import read_video
video, audio, info = read_video(item["video_path"])
```

---

## 🔹 WebDataset Version (Optional)

If the dataset includes `.tar` shards:

```python
import webdataset as wds, jsonlines

ids = [rec["video_path"] for rec in jsonlines.open("metadata_s2.jsonl")]
dset = wds.WebDataset("WebDataset_Shards/*.tar").select(lambda s: s["json"]["video_path"] in ids)
```

Each shard contains:

- `xxx.avi` → video bytes  
- `xxx.json` → metadata JSON  

---

## 🔹 PyTorch Example

```python
from torch.utils.data import Dataset, DataLoader
from decord import VideoReader

class BreakfastDataset(Dataset):
    def __init__(self, subset): self.subset = subset
    def __len__(self): return len(self.subset)
    def __getitem__(self, idx):
        item = self.subset[idx]
        vr = VideoReader(item["video_path"])
        frames = vr.get_batch([0, 8, 16])
        return frames, item["labels"]

loader = DataLoader(BreakfastDataset(ds), batch_size=4)
```

---

## 🔢 Splits Description

The dataset is partitioned by participant ID:

| Split | Participants |
|-------|--------------|
| **s1** | P03–P15 |
| **s2** | P16–P28 |
| **s3** | P29–P41 |
| **s4** | P42–P54 |

Each split has its own metadata JSONL file.

---

## 📚 Citation

If you use the Breakfast Actions dataset, please cite:

```bibtex
@inproceedings{kuehne2014language,
  title={The language of actions: Recovering the syntax and semantics of goal-directed human activities},
  author={Kuehne, Hildegard and Arslan, Ali and Serre, Thomas},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={780--787},
  year={2014}
}
```

---