Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,87 @@
+---
+license: other
+license_name: mixed-cc-by-nc-sa-4.0-and-mit
+tags:
+  - video-saliency
+  - shot-boundary-detection
+  - safetensors
+  - reframe
+library_name: safetensors
+---
+
+# 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.
+
+---
+
+## `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.
+
+---
+
+## `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.
+
+---
+
+## Verification recipe (reproducible)
+
+```python
+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.
+```