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    Models/Image Segmentation/smp-test-models/deeplabv3-tu-resnet18
    Image Segmentationsegmentation-models-pytorchmit

    deeplabv3-tu-resnet18

    by smp-test-models

    Identifier
    Model ID
    smp-test-models/deeplabv3-tu-resnet18

    Tags

    segmentation-models-pytorchsafetensorsmodel_hub_mixinpytorch_model_hub_mixinsemantic-segmentationpytorchimage-segmentationlicense:mitregion:us

    Use deeplabv3-tu-resnet18 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, deeplabv3-tu-resnet18 runs as a managed extractor inside a processing pipeline. Point a bucket of image segmentation 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.