# kandinsky-videomae-large-camera-motion by AI Forever Source: https://savrn.com/models/kandinsky-videomae-large-camera-motion Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## 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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.3 GB | 0.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [kandinsky-videomae-large-camera-motion on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/kandinsky-videomae-large-camera-motion/gpus) ## 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](https://huggingface.co/ai-forever/kandinsky-videomae-large-camera-motion) Released by AI Forever through its official repository on Hugging Face. ## Built From - Derived from [MCG-NJU/videomae-large](https://savrn.com/models/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. ## Similar Models Model · Video classification ### [videomae-large-finetuned-deepfake-subset](https://savrn.com/models/videomae-large-finetuned-deepfake-subset) [Mai](https://savrn.com/model-publishers/shylhy) 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 weights cc-by-nc-4.0 304M parameters transformers [View model](https://savrn.com/models/videomae-large-finetuned-deepfake-subset) Model · Video classification ### [VideoMAEv2-Large](https://savrn.com/models/videomaev2-large) [OpenGVLab](https://savrn.com/model-publishers/opengvlab) VideoMAEv2-Large model pre-trained for 800 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://arxiv.org/abs/2203.12602) by Wang et al. and first released in GitHub. You can use the raw model for video feature extraction. Here is how to use this model to extract a video feature Open weights cc-by-nc-4.0 304M parameters [View model](https://savrn.com/models/videomaev2-large) Model · Video classification ### [videomae-large-finetuned-kinetics](https://savrn.com/models/videomae-large-finetuned-kinetics) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to… Open weights cc-by-nc-4.0 304M parameters transformers [View model](https://savrn.com/models/videomae-large-finetuned-kinetics) Model · Video classification ### [vjepa2-vitl-fpc64-256](https://savrn.com/models/vjepa2-vitl-fpc64-256) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. To run V-JEPA 2 model, ensure you have installed the latest transformers: V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64. To load an image, simply copy the image to the desired number of frames. For more code examples, please refer to the V-JEPA 2… Open weights mit 326M parameters transformers [View model](https://savrn.com/models/vjepa2-vitl-fpc64-256) Model · Video classification ### [vjepa2.1-vit-large-384](https://savrn.com/models/vjepa2-1-vit-large-384) [Antonio Apicella](https://savrn.com/model-publishers/apiantonio) A HuggingFace-format conversion of Meta AI's V-JEPA 2.1 ViT-L/16 video encoder and predictor, operating at 384x384 resolution. The weights are Meta's, copied without modification. This repository provides the transformers-compatible packaging plus a documented numerical validation against the original implementation. An equivalent community port already exists (Dev-Jahn/vjepa2.1-vitl-fpc64-384). This repository adds an independently reproduced conversion together with the validation results below. Forward outputs match the existing port to all reported digits. The only structural change is that the fused QKV projection of each attention block is split into separate query / key / value… Open weights mit 328M parameters transformers [View model](https://savrn.com/models/vjepa2-1-vit-large-384) Model · Video classification ### [videomae-large](https://savrn.com/models/videomae-large) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of… Open weights cc-by-nc-4.0 343M parameters transformers [View model](https://savrn.com/models/videomae-large) ## AI Forever [All models and datasets](https://savrn.com/model-publishers/ai-forever) ## Versions - [3e9a74ff2524](https://savrn.com/models/kandinsky-videomae-large-camera-motion/versions/3e9a74ff2524) · current 2026-09-18 ## Explore More - [All video classification models](https://savrn.com/models/tasks/video-classification) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-21. - [Hugging Face record](https://huggingface.co/ai-forever/kandinsky-videomae-large-camera-motion) - [How the hub is built](https://savrn.com/model-hub/methodology)