Views
No views yet
torch.load(..., weights_only=True), flattening to a
plain {str: Tensor} state-dict, saving via safetensors.torch.save_file, then
reloading and proving exact tensor-equality (shape + dtype + torch.equal) on
every tensor against the original. No tensor values were altered — these are pure
re-containers of the upstream weights. The sha256 values below pin the hosted
safetensors bytes.Licenses differ per file (see each section). This repository is redistributed for personal / non-commercial use. The ViNet-S weight is CC BY-NC-SA 4.0 (ShareAlike); the TransNetV2 weight is MIT.
vinet-s-saliency.safetensors — ViNet-S video saliency (DHF1K)803e6d265d46d3f4f3d7ec2c6c2f3b4511f9ba176aa12e348ac317788ca0dc6812UeAsdiD2xPLmoLRDcE_HjAUjxFdmw5N, checkpoints.tar.gz, ~2.83 GiB),
member final_models/ViNet_S/vinet_s_visual_dataset_models/vinet_s_dhf1k.pt
(38,266,829 bytes, sha256 5d097a6b145b2cff7f08aa141a91e7aec4ac967504b439f4b04110c7e475cbbd).ViNet-S / ViNet++ saliency weights (c) 2025 Rohit Girmaji, Siddharth Jain, Bhav Beri, Sarthak Bansal, Vineet Gandhi (IIIT Hyderabad). "Minimalistic Video Saliency Prediction via Efficient Decoder & Spatio-Temporal Action Cues", ICASSP 2025 (arXiv:2502.00397). Licensed under CC BY-NC-SA 4.0. Redistributed here, re-containered to safetensors with tensor values unchanged, under the same CC BY-NC-SA 4.0 license (ShareAlike) for non-commercial use.
transnetv2.safetensors — TransNetV2 shot/scene-boundary detectore2877ef6750ccbb3f02256bb4b5f4f53035111677be641d56b9723af499f881dtransnetv2-pytorch-weights.pth
(30,508,183 bytes, sha256 834b10f25ae9e1b4e4f2652fe2843bd2b1388057a435d68b7c52635578fcc04d).convert_weights.py.apache-2.0; both MIT and Apache-2.0 permit redistribution with attribution. The
authoritative upstream license for these weights is MIT.TransNet V2 (c) Tomas Soucek & Jakub Lokoc. "TransNet V2: An Effective Deep Network Architecture for Fast Shot Transition Detection." Source: https://github.com/soCzech/TransNetV2 (MIT License). PyTorch weights converted from the upstream TensorFlow SavedModel; re-containered here to safetensors with tensor values unchanged.
1from safetensors.torch import load_file
2import torch
3sd = load_file("vinet-s-saliency.safetensors") # or transnetv2.safetensors
4# sd is a flat {str: torch.Tensor}; load_state_dict directly. No torch.load / pickle.