Autodistill: A new way to create CV models

• autodistill

  13 1,529 9.2 Python

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

  

Autodistill

• segment-anything

  56 44,026 4.2 Jupyter Notebook

  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

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Detic

  11 1,769 1.9 Python

  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

  5 4,978 6.6 Python

  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

  27 22,624 9.8 Python

  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

  8 4,322 9.6 Jupyter Notebook

  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

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