reframe-asd-weights

Verified, re-containered safetensors weights used by the Reframe vertical-video pipeline. Each file was produced offline in a trusted environment by unpickling the original author checkpoint with 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.

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vinet-s-saliency.safetensors — ViNet-S video saliency (DHF1K)

• File size: 38,138,372 bytes
• sha256: 803e6d265d46d3f4f3d7ec2c6c2f3b4511f9ba176aa12e348ac317788ca0dc68
• Tensors: 470, exact tensor-equality vs original VERIFIED
• Original source: author checkpoints bundle on Google Drive (file id 12UeAsdiD2xPLmoLRDcE_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).
• Upstream repo: https://github.com/ViNet-Saliency/vinet_v2
• License: CC BY-NC-SA 4.0 — https://creativecommons.org/licenses/by-nc-sa/4.0/

Attribution (required by CC BY-NC-SA 4.0):

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.

The DHF1K visual-only checkpoint is the general saliency model (no audio / no face dependency), appropriate for subject-tracking crops.

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transnetv2.safetensors — TransNetV2 shot/scene-boundary detector

• File size: 30,481,608 bytes
• sha256: e2877ef6750ccbb3f02256bb4b5f4f53035111677be641d56b9723af499f881d
• Tensors: 90, exact tensor-equality vs original VERIFIED
• Original source (bytes obtained from): HuggingFace mirror https://huggingface.co/Sn4kehead/TransNetV2 — transnetv2-pytorch-weights.pth (30,508,183 bytes, sha256 834b10f25ae9e1b4e4f2652fe2843bd2b1388057a435d68b7c52635578fcc04d).
• Upstream (canonical) repo: https://github.com/soCzech/TransNetV2 (MIT). The PyTorch weights are a derived artifact of the upstream TensorFlow SavedModel via the repo's convert_weights.py.
• License: MIT (soCzech/TransNetV2). Note: the Sn4kehead mirror card labels its copy apache-2.0; both MIT and Apache-2.0 permit redistribution with attribution. The authoritative upstream license for these weights is MIT.

Attribution:

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.

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Verification recipe (reproducible)

from safetensors.torch import load_file
import torch
sd = load_file("vinet-s-saliency.safetensors")   # or transnetv2.safetensors
# sd is a flat {str: torch.Tensor}; load_state_dict directly. No torch.load / pickle.