This model is a fine-tuned version of MCG-NJU/videomae-large on the Deepfake Detection Challenge dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - trainingsteps: 4470 - mixedprecisiontraining: Native AMP - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
Open-weight model · Video classification
kandinsky-videomae-large-camera-motion
by AI Forever ai-forever/kandinsky-videomae-large-camera-motion
VideoMAE model(large) variant that has been finetuned for multi-label video classification (a video can belong to multiple classes simultaneously) for camera motion classification on internal dataset.
Parameters304M
Context—
Weights1.2 GB
License—
AccessOpen weights
Monthly Downloads1.7k
Runs On
What it takes to serve kandinsky-videomae-large-camera-motion (304M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.6 GB | 0.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.3 GB | 0.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
VideoMAE model(large) variant that has been finetuned for multi-label video classification (a video can belong to multiple classes simultaneously) for camera motion classification on internal dataset. The model predicts 18 different camera motion 'arcleft', 'arcright', 'dollyin', 'dollyout', 'panleft', 'panright', 'pedestaldown', 'pedestalup', 'rollleft', 'rollright', 'static', 'tiltdown', 'tiltup', 'truckleft', 'truckright', 'undefined', 'zoomin', 'zoomout' and and 3 shot type classes: 'pov', 'shake', 'track'. Model was trained to associate entire video with camera labels, not frame-level motions(!): [input video] -> label/labels (because multilabel) for all video. So, if this camera…
Excerpt from the card by AI Forever.
Configuration
- Architecture
- VideoMAEForVideoClassification
- Layers
- 24
- Hidden size
- 1,024
- Feed-forward size
- 4,096
- Attention heads
- 16
- Stored precision
- float32
- Model type
- videomae
Identity and Version
- Repository
- ai-forever/kandinsky-videomae-large-camera-motion
- Publisher
- AI Forever
- Task
- Video classification
- Modality
- Video
- Library
- transformers
- Parameters
- 304M parameters
- Languages
- Not stated by the source
- Revision
- 3e9a74ff25249837e084ca23c4e31317ca014af3
- First published
- 2025-07-23
- Last updated
- 2025-07-23
Files and Weights
5 files, 1.2 GB in total. The weights are 1 file totalling 1.2 GB in safetensors.
Weights1 file · 1.2 GB
Configuration2 files · 2.2 KB
Documentation1 file · 1.9 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 1.2 GB | 7f76ada1a8fa |
| config.json | Configuration | 1.7 KB | — |
| preprocessor_config.json | Configuration | 415 B | — |
| README.md | Documentation | 1.9 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 1.2 GB
Download from AI Forever
Released by AI Forever through its official repository on Hugging Face.
Built From
- Derived from MCG-NJU/videomae-large
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.2 GB |
| 16-bit | 0.6 GB |
| 8-bit | 0.3 GB |
| 4-bit | 0.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About kandinsky-videomae-large-camera-motion
How much GPU memory does kandinsky-videomae-large-camera-motion need?
About 0.7 GB at 16-bit and 0.2 GB at 4-bit: the weights (304M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run kandinsky-videomae-large-camera-motion on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
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