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--- |
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dataset_info: |
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features: |
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- name: video_path |
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dtype: string |
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- name: label |
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dtype: string |
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- name: subset |
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dtype: int64 |
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splits: |
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- name: split1 |
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num_bytes: 636609 |
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num_examples: 6766 |
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- name: split2 |
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num_bytes: 636609 |
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num_examples: 6766 |
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- name: split3 |
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num_bytes: 636609 |
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num_examples: 6766 |
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download_size: 351201 |
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dataset_size: 1909827 |
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configs: |
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- config_name: default |
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data_files: |
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- split: split1 |
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path: data/split1-* |
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- split: split2 |
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path: data/split2-* |
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- split: split3 |
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path: data/split3-* |
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--- |
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# 📘 HMDB51 Dataset (with Protocol Splits + Video Streaming Support) |
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This repository hosts the **HMDB51** human action recognition dataset in a format optimized for modern deep learning research. |
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It provides: |
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- Three official evaluation protocols (`split1`, `split2`, `split3`) |
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- JSONL metadata files containing action labels and train/test assignments |
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- Raw video files stored directly on HuggingFace Hub |
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- Optional **WebDataset** tar shards for high-performance streaming |
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--- |
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## 📁 Folder Layout |
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``` |
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HMDB51/ |
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│ |
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├── metadata_split1.jsonl |
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├── metadata_split2.jsonl |
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├── metadata_split3.jsonl |
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│ |
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├── Videos/ |
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│ ├── brush_hair/ |
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│ ├── climb/ |
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│ └── ... (all 51 classes) |
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│ |
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└── webdataset/ |
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├── 000000.tar |
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├── 000001.tar |
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└── ... |
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``` |
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Each JSONL record: |
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```json |
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{ |
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"video_path": "Videos/brush_hair/example.avi", |
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"label": "brush_hair", |
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"subset": 1 |
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} |
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``` |
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--- |
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## 🔹 1. Load Metadata (HF-native) |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("json", data_files="metadata_split2.jsonl")["train"] |
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train = ds.filter(lambda x: x["subset"] == 1) |
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test = ds.filter(lambda x: x["subset"] == 2) |
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``` |
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--- |
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## 🔹 2. Load a Video File |
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### Decord |
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```python |
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from decord import VideoReader |
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vr = VideoReader(train[0]["video_path"]) |
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frame0 = vr[0] |
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``` |
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### TorchVision |
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```python |
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from torchvision.io import read_video |
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video, audio, info = read_video(train[0]["video_path"]) |
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``` |
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--- |
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## 🔹 3. WebDataset Version (Optional) |
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```python |
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import webdataset as wds, jsonlines |
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ids = [rec["video_path"] for rec in jsonlines.open("metadata_split2.jsonl") if rec["subset"]==1] |
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train_wds = wds.WebDataset("webdataset/*.tar").select(lambda s: s["__key__"] in ids) |
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``` |
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--- |
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## 🔹 4. PyTorch DataLoader Example |
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```python |
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from torch.utils.data import Dataset, DataLoader |
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from decord import VideoReader |
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class VideoDataset(Dataset): |
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def __init__(self, subset): self.subset = subset |
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def __getitem__(self, i): |
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item = self.subset[i] |
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vr = VideoReader(item["video_path"]) |
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return vr.get_batch([0,8,16]), item["label"] |
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def __len__(self): return len(self.subset) |
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loader = DataLoader(VideoDataset(train), batch_size=4) |
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``` |
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--- |
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## 🔹 5. Protocol Files |
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``` |
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metadata_split1.jsonl |
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metadata_split2.jsonl |
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metadata_split3.jsonl |
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``` |
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Each matches the official HMDB51 evaluation protocol. |
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--- |
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## 📚 Citation |
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```bibtex |
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@inproceedings{kuehne2011hmdb, |
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title={HMDB: a large video database for human motion recognition}, |
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author={Kuehne, Hildegard and Jhuang, Hueihan and Garrote, Est{'\i}baliz and Poggio, Tomaso and Serre, Thomas}, |
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booktitle={2011 International conference on computer vision}, |
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pages={2556--2563}, |
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year={2011}, |
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organization={IEEE} |
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} |
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``` |
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--- |
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|
