Downloads · 30 days
0
DotCheck/muybridge-video-v4_4
muybridge-video-v4_4 is a image classification model from DotCheck. Use it when you need a label for an image. It is set up for dotcheck. The card lists the license as apache-2.0.
[email protected] (inhouse-video@4) is a video-frame detector. A clip is reduced to a small set of inner-frame stamps. Each stamp is scored with the same pair geometry as Vermeer stills. The clip score is the maximum stam…
Downloads · 30 days
0
Access
Public
Updated Sep 6, 2026
Repo size
3 GB
Likes
0
Public
Click a slice to open those files.
.safetensors3 GB · 100%
From the Hugging Face model README
[email protected] (inhouse-video@4) is a video-frame detector. A clip is reduced to a small set of inner-frame stamps. Each stamp is scored with the same pair geometry as Vermeer stills. The clip score is the maximum stamp probability (stamp-cap), not a mean and not bag p90.
The pair stack is Plateau 3.8 on the transport view plus Blau 1.4 on the native center crop, then a two-logit logistic combiner. The LoRA spines are the same files as [email protected]; the video heads and the 4.4 combiner are specific to this repository. SigLIP 2 is the frozen ancestor. The previous public video repository muybridge-video-v2 remains an archive and is not overwritten.
Product paths that fuse frame scores with Helmholtz soundtrack windows (Covenant) are not this model and have no public fused claim table. The numbers below are frames-only stamp-cap.
| Field | Value |
|---|---|
| Hub id | DotCheck/muybridge-video-v4_4 |
| Wire id | inhouse-video@4 |
| Public token | [email protected] |
| Combiner artifact | siglip2_base_patch16_224_linear_head_v4_4.npz |
| Transport head | siglip2_base_patch16_224_linear_head_v3_8.npz (Plateau 3.8) |
| Center head | siglip2_base_patch16_224_linear_head_v1_4.npz (Blau 1.4) |
| Shared center arm (serve) | siglip2_base_patch16_224_linear_head_v6_6.npz |
| Spines | same 13.12 + 6.6 safetensors as [email protected] |
| Backbone | google/siglip2-base-patch16-224 (Apache-2.0) |
| Stamp count | 2 inner frames (33% / 67%) if duration (\le 180,\mathrm{s}); else (\min(12,\ \max(2,\ \lfloor d/60\rfloor))) |
| Clip fold | (\max_i p_i) (stamp-cap) |
| Encode | same pair views as stills (see below) |
| Output | (p \in [0,1]) — estimated (P(\mathrm{AI})) |
| Serve | CPU FastAPI POST /v1/analyze-pair kind=video |
For each extracted stamp, encoding is identical to Vermeer stills (train = serve):
Stamp probability:
[ p_i = \sigma(w_P z_P + w_B z_B + b) ]
Locked 4.4 weights: (w = (0.7773,\ 0.9751)), (b = -0.4967). Clip score (p = \max_i p_i).
This is not a temporal network. Motion is represented only by which frames are sampled.
In this repo: README.md, LICENSE, NOTICE, CITATION.cff, the 4.4 combiner .npz, the video arm heads, the shared center arm, and both spine safetensors.
clip duration d
stamp times: 2 inner (33%, 67%) if d ≤ 180 s
else min(12, max(2, floor(d/60)))
for each stamp raster:
reject if min(W, H) < 224
transport: max side ≤ 256, aspect kept, JPEG q70 → Plateau v3.8 → z_P
center: 224×224 native crop, JPEG q85 → Blau v1.4 → z_B
p_i = σ(0.7773 z_P + 0.9751 z_B − 0.4967)
clip p = max(p_i)
Each stamp is two frozen SigLIP 2 forwards (same pair geometry as Vermeer), then the 4.4 logistic. The clip score is the maximum stamp probability.
Open weights: the combiner .npz, video heads, shared center arm, and spines in this repository (Apache-2.0), used with the frozen SigLIP 2 backbone named above. This is not a transformers AutoModel package.
Product scoring: Check or Pro API. Hub storefront: see-whats-real.
Fit uses commercial-clean open video generators under license and hardware constraints (including CogVideoX-2b and Wan2.1, Apache-2.0) plus other commercial-clean families in the Muybridge mix. Unique parent = clip bag. Training does not include waveform audio.
The public evaluation set (Layer A, pair2 stamps, stamp-cap):
| Class | n (bags) |
|---|---|
| Real | 602 |
| AI | 180 |
Protocol notes: eval/MUYBRIDGE_4.md. Artifact names: RULER_video_v4_4.json, video_gates_v4_4.json, MUYBRIDGE_CARD_v4_4.json.
Binary classification at threshold (0.5). Public claim = Layer A stamp-cap class-conditional mean (P(\mathrm{AI})) and balanced accuracy.
| Metric | n | Measured |
|---|---|---|
| mean (P(\mathrm{AI})) | real | 602 | 0.022 |
| mean (P(\mathrm{AI})) | AI | 180 | 0.976 |
| balanced accuracy | 0.9859 |
The 25-family still gold panel is a Vermeer exam, not a 4.4 public claim. Family slices with (n < 20) are report-only.
Out of scope: temporal localization, generator attribution, and fused frame+audio scores (Covenant). The public table is frames-only stamp-cap.
LICENSE — Apache License 2.0 for DotCheck heads and spines in this repository. Upstream backbone: NOTICE.
CITATION.cff · [email protected] · wire inhouse-video@4 · https://dotcheck.ai/docs