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    Models/Object Detection/jameslahm/yolov10m
    Object Detectionyolov10agpl-3.0

    yolov10m

    by jameslahm

    Identifier
    Model ID
    jameslahm/yolov10m

    Tags

    yolov10safetensorsobject-detectioncomputer-visionpytorch_model_hub_mixindataset:detection-datasets/cocoarxiv:2405.14458license:agpl-3.0region:us

    Use yolov10m 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, yolov10m runs as a managed extractor inside a processing pipeline. Point a bucket of object detection 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.