NEWVectors or files. Pick a path.Start →
    Models/Image Classification/harrytechiz/vit-base-patch16-224-blur_vs_clean
    Image Classificationtransformersapache-2.0

    vit-base-patch16-224-blur_vs_clean

    by harrytechiz

    Identifier
    Model ID
    harrytechiz/vit-base-patch16-224-blur_vs_clean

    Tags

    transformerspytorchtensorboardvitimage-classificationgenerated_from_trainerdataset:imagefolderbase_model:google/vit-base-patch16-224base_model:finetune:google/vit-base-patch16-224license:apache-2.0model-indexendpoints_compatibleregion:us

    Use vit-base-patch16-224-blur_vs_clean 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, vit-base-patch16-224-blur_vs_clean 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.