Use Azure Vision Studio to train a fine-tuned object detection model that you own.

Open Flamingo module for use with Autodistill.

RaidexAI is a Next.js application integrated with a machine learning model, YOLOv8, to detect anomalies in radiology images. The output is sent to a Flask server, which uses OpenCV to create Regions of Interest (ROIs) in the images and employs a Language Model (LLM) for contextual analysis, interpreting the content alongside the detected anomalies

Use AWS Rekognition to train custom models that you own.

FastViT base model for use with Autodistill.

Autodistill Google Cloud Vision module for use in training a custom, fine-tuned model.

AltCLIP model for use with Autodistill.

EfficientSAM base model for use with Autodistill.

Qwen-VL base model for use with Autodistill.

A template for use in creating Autodistill Target Model packages.

Application de détection de bateaux à partir de vues aériennes utilisant Yolov8.

DETR (resnet-50) module for use with Autodistill.

YOLOv5 module for use with Autodistill.

BioCLIP base model for use with Autodistill.

Use Segment Anything to identify objects in an image.

CoDet base model for use with Autodistill.

ALBEF module for use with Autodistill.

Fuyu multi-modal language model for use with Autodistill.

An intuitive Python tool for annotating images with bounding boxes. Easily assign custom classes to objects and save annotations. Includes AI model integration for automated annotation. Perfect for streamlining computer vision projects. classes to these objects, and save annotations.

Use the HLS Geospatial model made by NASA and IBM to generate masks for use in training a fine-tuned segmentation model.