The scenes are tagged according to 34 distinct scene tags, and all objects are tracked throughout the scene to promote a wide range of applications.

We also gathered milk yield data and outdoor weather conditions.

Additionally, we provide multimodal predictive models combining video, audio, and physiological inputs.

Autonomous trucking is a promising technology that can greatly impact modern logistics and the environment. Ensuring its safety on public roads is one of the main duties that requires an accurate perception of the environment. To achieve this, machine learning methods rely on large datasets, but to this day, no such datasets are available for autonomous trucks.

StressID is a new dataset specifically designed for stress identification fromunimodal and multimodal data. It contains videos of facial expressions, audiorecordings, and physiological signals. The video and audio recordings are acquiredusing an RGB camera with an integrated microphone. The physiological datais composed of electrocardiography (ECG), electrodermal activity (EDA), andrespiration signals that are recorded and monitored using a wearable device. Thisexperimental setup ensures a synchronized and high-quality multimodal data col-lection. Different stress-inducing stimuli, such as emotional video clips, cognitivetasks including mathematical or comprehension exercises, and public speakingscenarios, are designed to trigger a diverse range of emotional responses. Thefinal dataset consists of recordings from 65 participants who performed 11 tasks,as well as their ratings of perceived relaxation, stress, arousal, and valence levels.StressID is one of the largest datasets for stress identification that features threedifferent sources of data and varied classes of stimuli, representing more than39 hours of annotated data in total. StressID offers baseline models for stressclassification including a cleaning, feature extraction, and classification phase foreach modality. Additionally, we provide multimodal predictive models combiningvideo, audio, and physiological inputs.

Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms. To address this shortcoming in the machine learning ecosystem, we introduce DataComp, a testbed for dataset experiments centered around a new candidate pool of 12.8 billion image-text pairs from Common Crawl. Participants in our benchmark design new filtering techniques or curate new data sources and then evaluate their new dataset by running our standardized CLIP training code and testing the resulting model on 38 downstream test sets. Our benchmark consists of multiple compute scales spanning four orders of magnitude, which enables the study of scaling trends and makes the benchmark accessible to researchers with varying resources. Our baseline experiments show that the DataComp workflow leads to better training sets. Our best baseline, DataComp-1B, enables training a CLIP ViT-L/14 from scratch to 79.2% zero-shot accuracy on ImageNet, outperforming OpenAI's CLIP ViT-L/14 by 3.7 percentage points while using the same training procedure and compute. We release \datanet and all accompanying code at www.datacomp.ai.

Massive web datasets play a key role in the success of large vision-language models like CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to reduce noise often come at the expense of data diversity. Our work focuses on caption quality as one major source of noise, and studies how generated captions can increase the utility of web-scraped datapoints with nondescript text. Through exploring different mixing strategies for raw and generated captions, we outperform the best filtering method proposed by the DataComp benchmark by 2% on ImageNet and 4% on average across 38 tasks, given a candidate pool of 128M image-text pairs. Our best approach is also 2x better at Flickr and MS-COCO retrieval. We then analyze what makes synthetic captions an effective source of text supervision. In experimenting with different image captioning models, we also demonstrate that the performance of a model on standard image captioning benchmarks (e.g., NoCaps CIDEr) is not a reliable indicator of the utility of the captions it generates for multimodal training. Finally, our experiments with using generated captions at DataComp's large scale (1.28B image-text pairs) offer insights into the limitations of synthetic text, as well as the importance of image curation with increasing training data quantity. The synthetic captions used in our experiments are now available on HuggingFace.