Model Hub
Browse PQC-verified AI models, datasets, and tools
Summary This is the dataset proposed in our paper [ICLR 2025] OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation. OpenVid-1M is a high-quality text-to-video dataset designed for research institutions to enhance video quality, featuring high aesthetics, clarity, and resolution. It can be used for direct training or as a quality tuning complement to other video datasets. All videos in the OpenVid-1M dataset have resolutions of at least 512×512.… See the full description on the dataset page: https://huggingface.co/datasets/nkp37/OpenVid-1M.
Dataset Card for "rotten_tomatoes" Dataset Summary Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005. Supported Tasks and Leaderboards More Information Needed Languages… See the full description on the dataset page: https://huggingface.co/datasets/cornell-movie-review-data/rotten_tomatoes.
Dataset Card for Fish-Visual Trait Analysis (Fish-Vista) Note that the '</Use this dataset>' option will only load the CSV files. To download the entire dataset, including all processed images and segmentation annotations, refer to Instructions for downloading dataset and images. See Example Code to Use the Segmentation Dataset Figure 1. A schematic representation of the different tasks in Fish-Vista Dataset. Instructions for downloading dataset… See the full description on the dataset page: https://huggingface.co/datasets/saffatgazi/fish-vista.
PQC-signed hypervisor memory attestation framework for AI workloads. ML-DSA signed claims about memory region state, drift detection, pluggable backends for AMD SEV-SNP and Intel TDX. Protects model weights and activations on shared cloud infrastructure. 26 tests passing.
PQC Secure Enclave SDK for on-device AI. ML-KEM-768 key encapsulation + AES-256-GCM encrypted model weights, credentials, adapters, biometric templates. Pluggable backends for Apple Secure Enclave, Android StrongBox, Qualcomm QSEE. ML-DSA device attestation for proof of enclave storage. Protects 5+ year HNDL exposure of on-device weights. 32 tests passing.