Autodistill: A new way to create CV models

This page summarizes the projects mentioned and recommended in the original post on /r/developersIndia

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  • autodistill

    Images to inference with no labeling (use foundation models to train supervised models).

    Autodistill

  • segment-anything

    The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

    Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • Detic

    Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

    Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO

  • GroundingDINO

    Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

    Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO

  • ultralytics

    NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

    And the target models include: * YOLOv8 (You Only Look Once) * YOLO-NAS * YOLOv5 * and DETR

  • super-gradients

    Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

    And the target models include: * YOLOv8 (You Only Look Once) * YOLO-NAS * YOLOv5 * and DETR

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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