# ms-eff-gcvit-deepfake-b5-ff-plus-plus by YUNJE SEO Source: https://savrn.com/models/ms-eff-gcvit-deepfake-b5-ff-plus-plus Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve ms-eff-gcvit-deepfake-b5-ff-plus-plus (53M 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 | 0.1 GB | 0.1 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 | 0.1 GB | 0.1 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 | 0.0 GB | 0.0 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 24, 2026. [ms-eff-gcvit-deepfake-b5-ff-plus-plus on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-ff-plus-plus/gpus) ## Model Card By YUNJE SEO, published under mit, revision a8fd324d86ef. ### Multi Scale Efficient Global Context Vision Transformer GitHub Repository: [HanMoonSub/DeepGuard](https://github.com/HanMoonSub/DeepGuard) Live demo: [DeepFake Video Detection](https://huggingface.co/spaces/KoreaPeter/DeepFake-Vidoe-Detection) Live demo: [DeepFake Image Detection](https://huggingface.co/spaces/KoreaPeter/DeepFake-Image-Detection) Live demo: [DeepFake Detection XAI](https://huggingface.co/spaces/KoreaPeter/DeepFake-Detection-XAI) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. ### Core Features - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for real-time/edge, Pro (b5) for enterprise accuracy. - timm-compatible — load via the timm interface or the deepguard package. ### Model Specifications [Read the full model card (1,000 words)](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-ff-plus-plus/card) ## Configuration Architecture MsEffGCViTForImageClassification Model type ms_eff_gcvit ## Identity and Version Repository KoreaPeter/ms-eff-gcvit-deepfake-b5-ff-plus-plus Publisher YUNJE SEO Task Video classification Modality Video Library transformers Parameters 53M parameters Languages Not stated by the source Revision a8fd324d86ef7e4caeb2a693e1207d794be32b9f First published 2026-06-23 Last updated 2026-07-04 ## Files and Weights 12 files, 229.0 MB in total. The weights are 2 files totalling 226.5 MB in pt, safetensors. Weights2 files · 226.5 MB Configuration5 files · 13.2 KB Documentation1 file · 11.4 KB Other3 files · 2.5 MB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 220.1 MB | 843a63c3359d | | yolov8n-face.pt | Weights | 6.4 MB | d545bf1add5a | | config.json | Configuration | 1.5 KB | — | | configuration_ms_eff_gcvit.py | Configuration | 1.5 KB | — | | modeling_ms_eff_gcvit.py | Configuration | 1.3 KB | — | | pipeline_ms_eff_gcvit.py | Configuration | 3.5 KB | — | | pipeline_video_ms_eff_gcvit.py | Configuration | 5.4 KB | — | | README.md | Documentation | 11.4 KB | — | | dual_branch.gif | Other | 2.3 MB | 3b17745fc2ef | | ms_eff_gcvit.JPG | Other | 100.3 KB | 9cc6e353e563 | | window_attention.JPG | Other | 45.2 KB | — | | .gitattributes | Repository | 1.6 KB | — | ## License and Download License mit Access Open weights, no gate Download size 226.5 MB [Download from YUNJE SEO](https://huggingface.co/KoreaPeter/ms-eff-gcvit-deepfake-b5-ff-plus-plus) Released by YUNJE SEO through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Derived from timm/tf_efficientnet_b5.ns_jft_in1k - Described by [arXiv:1901.08971](https://savrn.com/papers/faceforensics-learning-to-detect-manipulated-facial-images) - Trained on (disclosed) [ILSVRC/imagenet-1k](https://savrn.com/datasets/imagenet-1k) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 226.5 MB | | 16-bit | 0.1 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About ms-eff-gcvit-deepfake-b5-ff-plus-plus ### How much GPU memory does ms-eff-gcvit-deepfake-b5-ff-plus-plus need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (53M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run ms-eff-gcvit-deepfake-b5-ff-plus-plus 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. ### Can I use ms-eff-gcvit-deepfake-b5-ff-plus-plus commercially? Yes. ms-eff-gcvit-deepfake-b5-ff-plus-plus is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ## Similar Models Model · Video classification ### [ms-eff-gcvit-deepfake-b5-celeb-df-v2](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-celeb-df-v2) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 53M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-celeb-df-v2) Model · Video classification ### [ms-eff-gcvit-deepfake-b5-kodf](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-kodf) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 53M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-kodf) Model · Video classification ### [VideoMAEv2-Base](https://savrn.com/models/videomaev2-base) [OpenGVLab](https://savrn.com/model-publishers/opengvlab) VideoMAEv2-Base 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 86M parameters [View model](https://savrn.com/models/videomaev2-base) Model · Video classification ### [videomae-base-finetuned-kinetics-violence-nonviolence-tuned](https://savrn.com/models/videomae-base-finetuned-kinetics-violence-nonviolence-tuned) [Limon Alfredo](https://savrn.com/model-publishers/cliffer1) This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - trainingsteps: 348 - Transformers 4.49.0 - Pytorch 2.6.0+cu126 - Datasets 3.3.2 - Tokenizers 0.21.0 Open weights cc-by-nc-4.0 86M parameters transformers [View model](https://savrn.com/models/videomae-base-finetuned-kinetics-violence-nonviolence-tuned) Model · Video classification ### [videomae-violence-detector](https://savrn.com/models/videomae-violence-detector) [Oleg Radzhabov](https://savrn.com/model-publishers/happygook) This model is a fine-tuned version of MCG-NJU/videomae-base for binary violence classification (violent / non-violent). It builds on Nikeytas/videomae-crime-detector-production-v1, which was itself fine-tuned from videomae-base on a subset of UCF Crime. Starting from that checkpoint, this model was further fine-tuned on the Bus Violence Dataset to close the domain gap to public-transport surveillance footage. - UCF Crime (jinmang2/ucfcrime) — inherited from the base checkpoint - Bus Violence Dataset (Zenodo) — real moving-bus footage, binary violent / non-violent labels, used for domain-specific fine-tuning Evaluated on a held-out Bus Violence Dataset test split (n = 280). The base… Open weights mit 86M parameters [View model](https://savrn.com/models/videomae-violence-detector) Model · Video classification ### [videomae-base-finetuned-ucf101-subset](https://savrn.com/models/videomae-base-finetuned-ucf101-subset) [Hon Nguyen](https://savrn.com/model-publishers/tihon-nth) This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown 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 - trainingsteps: 370 - Transformers 5.16.1 - Pytorch 2.14.0+cu126 - Datasets 5.0.1 - Tokenizers 0.23.2 Open weights cc-by-nc-4.0 86M parameters transformers [View model](https://savrn.com/models/videomae-base-finetuned-ucf101-subset) ## YUNJE SEO [All models and datasets](https://savrn.com/model-publishers/koreapeter) ## Versions - [a8fd324d86ef](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-ff-plus-plus/versions/a8fd324d86ef) · current 2026-09-18 ## Explore More - [All video classification models](https://savrn.com/models/tasks/video-classification) - [All models under mit](https://savrn.com/models/licenses/mit) - [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/KoreaPeter/ms-eff-gcvit-deepfake-b5-ff-plus-plus) - [How the hub is built](https://savrn.com/model-hub/methodology)