datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation
Dataset Card for Dataset SADC
There is evidence that the driving style of an
autonomous vehicle is important to increase the acceptance
and trust of the passengers. The driving situation has been
found to have a significant influence on human driving behavior.
However, current driving style models only partially incorporate
driving environment information, limiting the alignment between
an agent and the given situation.
Therefore, we propose a dataset for situation-aware… See the full description on the dataset page: https://huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation
Dataset Card for Dataset SADC
There is evidence that the driving style of an
autonomous vehicle is important to increase the acceptance
and trust of the passengers. The driving situation has been
found to have a significant influence on human driving behavior.
However, current driving style models only partially incorporate
driving environment information, limiting the alignment between
an agent and the given situation.
Therefore, we propose a dataset for situation-aware driving… See the full description on the dataset page: https://huggingface.co/datasets/zzqasdfsdf/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.GCA_parallel_adaptation_b01This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "panda",
"total_episodes": 50,
"total_frames": 13104,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/hyzhang01/GCA_parallel_adaptation_b01.microcolony-domain-adaptationMicrocolony Domain Adaptation (Foodborne Bacteria) is a microscopy image dataset for foodborne bacterial classification under varying imaging conditions. It was created to support research in adversarial domain adaptation, enabling models trained on standard phase contrast microscopy images to generalize across different optical configurations and biological conditions.
This dataset accompanies the publication: Bhattacharya, S., Wasit, A., Earles, M., Nitin, N., & Yi, J. (2025). Enhancing AI… See the full description on the dataset page: https://huggingface.co/datasets/food-ai-nexus/microcolony-domain-adaptation.OVCLctta_data_repo