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    Models/Image Classification/Mooshie/swinv2_base_window8_256.dbv4-full

    swinv2_base_window8_256.dbv4-full

    by Mooshie

    Identifier
    Model ID
    Mooshie/swinv2_base_window8_256.dbv4-full

    Deploy swinv2_base_window8_256.dbv4-full

    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

    timmonnxsafetensorsimage-classificationtransformersanimetimmdghs-imgutilsdataset:animetimm/danbooru-wdtagger-v4-w640-ws-fullbase_model:timm/swinv2_base_window8_256.ms_in1kbase_model:quantized:timm/swinv2_base_window8_256.ms_in1klicense:gpl-3.0region:us

    Use swinv2_base_window8_256.dbv4-full 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, swinv2_base_window8_256.dbv4-full runs as a managed extractor inside a processing pipeline. Point a bucket of image 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.