TransNetV2 (ONNX)

transnetv2.onnx — an ONNX export of TransNetV2 for shot boundary detection (finding the hard cuts and gradual transitions between the shots of a video). Runs client-side with onnxruntime-web.

Inputs / outputs

• Input: input float32 [1, 100, 27, 48, 3] — a window of 100 frames, each resized to 48x27, RGB, values 0..255 (the graph divides by 255 itself).
• Outputs: two float32 [1, 100, 1] per-frame probabilities (the sigmoid is already applied):
  • single-frame — the frame a cut lands on (the last frame of the outgoing shot);
  • many-hot — every frame inside a transition (dissolve, fade, …).
• Upstream inference slides the window by 50 frames with 25 frames of context either side and keeps the middle 50 predictions of each window.

Provenance & license

• Model: TransNetV2 — TransNet V2: An effective deep network architecture for fast shot transition detection (Souček & Lokoč, 2020), soCzech/TransNetV2. Code and model weights are released under the MIT license (Copyright (c) 2020 Tomáš Souček), included here as LICENSE.
• ONNX export: elya5/transnetv2 (MIT), mirrored verbatim.