Update README.md

README.md

@@ -44,3 +44,177 @@ configs:
   - 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}
+}
+```
+
+---
+
+## ✔ Summary
+
+- 4 official splits (s1–s4)  
+- Metadata in JSONL → converted to parquet automatically on HF  
+- Videos usable directly on HF without full download  
+- Optional WebDataset tar shards for high-speed streaming  
+- Ready for video classification and temporal segmentation tasks  
+
+This setup is ideal for research, prototyping, and reproducible benchmarking.
+
+