metadata
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:
{
"video_path": "Videos/brush_hair/example.avi",
"label": "brush_hair",
"subset": 1
}
🔹 1. Load Metadata (HF-native)
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
from decord import VideoReader
vr = VideoReader(train[0]["video_path"])
frame0 = vr[0]
TorchVision
from torchvision.io import read_video
video, audio, info = read_video(train[0]["video_path"])
🔹 3. WebDataset Version (Optional)
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
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
@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}
}