MmCows: A Multimodal Dataset for Dairy Cattle Monitoring

MmCows is a large-scale multimodal dataset for behavior monitoring, health management, and dietary management of dairy cattle.

The dataset consists of data from 16 dairy cows collected during a 14-day real-world deployment, divided into two modality groups. The primary group includes 3D UWB location, cows' neck IMMU acceleration, air pressure, cows' CBT, ankle acceleration, multi-view RGB images, indoor THI, outdoor weather, and milk yield. The secondary group contains measured UWB distances, cows' head direction, lying behavior, and health records.

MmCows also contains 20,000 isometric-view images from multiple camera views in one day that are annotated with cows' ID and their behavior as the ground truth. The annotated cow IDs from multi-views are used to derive their 3D body location ground truth.

More details of the dataset and benchmarks are available at https://github.com/neis-lab/mmcows.

Brief overview video: https://www.youtube.com/watch?v=YBDvz-HoLWg

1. Install requirements

pip install huggingface_hub

2. Download a file individually

See the file structure here

Using command line:

huggingface-cli download \
  neis-lab/mmcows \
    visual_data.zip \
  --repo-type dataset \
  --local-dir ./

Using a Python script:

from huggingface_hub import hf_hub_download
hf_hub_download(
    repo_id="neis-lab/mmcows",
    repo_type="dataset",
    local_dir="./",
    filename="visual_data.zip"
)

3. Download all files inside a folder

Using command line:

huggingface-cli download \
  neis-lab/mmcows \
  --repo-type dataset \
  --include "1s_interval_images_3hr/*" \
  --local-dir ./