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    Models/Video Classification/KoreaPeter/ms-eff-gcvit-deepfake-b5-kodf
    Video Classificationtransformersmit

    ms-eff-gcvit-deepfake-b5-kodf

    by KoreaPeter

    Identifier
    Model ID
    KoreaPeter/ms-eff-gcvit-deepfake-b5-kodf

    Deploy ms-eff-gcvit-deepfake-b5-kodf

    Single-tenant

    Mixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.

    Tags

    transformerssafetensorsms_eff_gcvitimage-classificationPyTorchvisionDeepFake-DetectionDeepGuardtf-efficientnetglobal-context-vision-transformervideo-classificationcustom_codedataset:ILSVRC/imagenet-1kbase_model:timm/tf_efficientnet_b5.ns_jft_in1kbase_model:finetune:timm/tf_efficientnet_b5.ns_jft_in1klicense:mitregion:us

    Use ms-eff-gcvit-deepfake-b5-kodf on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

    Open Studio

    How It Runs on Mixpeek

    On Mixpeek, ms-eff-gcvit-deepfake-b5-kodf runs as a managed extractor inside a processing pipeline. Point a bucket of video classification data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.