README.md

---
dataset_info:
  features:
  - name: video_path
    dtype: string
  - name: label
    dtype: string
  - name: subset
    dtype: int64
  splits:
  - name: split1
    num_bytes: 636609
    num_examples: 6766
  - name: split2
    num_bytes: 636609
    num_examples: 6766
  - name: split3
    num_bytes: 636609
    num_examples: 6766
  download_size: 351201
  dataset_size: 1909827
configs:
- config_name: default
  data_files:
  - split: split1
    path: data/split1-*
  - split: split2
    path: data/split2-*
  - split: split3
    path: data/split3-*
---
# 📘 HMDB51 Dataset (with Protocol Splits + Video Streaming Support)

This repository hosts the **HMDB51** human action recognition dataset in a format optimized for modern deep learning research.  
It provides:

- Three official evaluation protocols (`split1`, `split2`, `split3`)
- JSONL metadata files containing action labels and train/test assignments
- Raw video files stored directly on HuggingFace Hub
- Optional **WebDataset** tar shards for high-performance streaming

---

## 📁 Folder Layout

```
HMDB51/
│
├── metadata_split1.jsonl
├── metadata_split2.jsonl
├── metadata_split3.jsonl
│
├── Videos/
│    ├── brush_hair/
│    ├── climb/
│    └── ... (all 51 classes)
│
└── webdataset/
     ├── 000000.tar
     ├── 000001.tar
     └── ...
```

Each JSONL record:

```json
{
  "video_path": "Videos/brush_hair/example.avi",
  "label": "brush_hair",
  "subset": 1
}
```

---

## 🔹 1. Load Metadata (HF-native)

```python
from datasets import load_dataset
ds = load_dataset("json", data_files="metadata_split2.jsonl")["train"]
train = ds.filter(lambda x: x["subset"] == 1)
test  = ds.filter(lambda x: x["subset"] == 2)
```

---

## 🔹 2. Load a Video File

### Decord

```python
from decord import VideoReader
vr = VideoReader(train[0]["video_path"])
frame0 = vr[0]
```

### TorchVision

```python
from torchvision.io import read_video
video, audio, info = read_video(train[0]["video_path"])
```

---

## 🔹 3. WebDataset Version (Optional)

```python
import webdataset as wds, jsonlines
ids = [rec["video_path"] for rec in jsonlines.open("metadata_split2.jsonl") if rec["subset"]==1]
train_wds = wds.WebDataset("webdataset/*.tar").select(lambda s: s["__key__"] in ids)
```

---

## 🔹 4. PyTorch DataLoader Example

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

class VideoDataset(Dataset):
    def __init__(self, subset): self.subset = subset
    def __getitem__(self, i):
        item = self.subset[i]
        vr = VideoReader(item["video_path"])
        return vr.get_batch([0,8,16]), item["label"]
    def __len__(self): return len(self.subset)

loader = DataLoader(VideoDataset(train), batch_size=4)
```

---

## 🔹 5. Protocol Files

```
metadata_split1.jsonl
metadata_split2.jsonl
metadata_split3.jsonl
```

Each matches the official HMDB51 evaluation protocol.

---

## 📚 Citation

```bibtex
@inproceedings{kuehne2011hmdb,
  title={HMDB: a large video database for human motion recognition},
  author={Kuehne, Hildegard and Jhuang, Hueihan and Garrote, Est{'\i}baliz and Poggio, Tomaso and Serre, Thomas},
  booktitle={2011 International conference on computer vision},
  pages={2556--2563},
  year={2011},
  organization={IEEE}
}
```

---