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Ikomia HUB

Ready to use Computer Vision algorithms and Apps.

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Inference with YOLO26 pose estimation models

Keypoints detection
1.0.0
AGPL-3.0
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Train YOLO26 classification models.

Classification
1.0.0
AGPL-3.0
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Inference with YOLO26 image classification models

Classification
1.0.0
AGPL-3.0
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Train YOLO26 segmentation models.

Instance segmentation
1.0.0
AGPL-3.0
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Inference with YOLO26 segmentation models

Instance segmentation
1.0.0
AGPL-3.0
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Train YOLO26 object detection models.

Object detection
1.0.0
AGPL-3.0
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Inference with YOLO26 models (Ultralytics)

Object detection
1.0.0
AGPL-3.0
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Face analysis with detection, age, gender, and emotion prediction using UniFace

Object detection
1.0.0
MIT
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Face parsing (semantic segmentation) using UniFace BiSeNet model.

Semantic segmentation
1.0.0
MIT
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Face verification using UniFace - verify if two faces belong to the same person

1.0.0
MIT
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Face search using UniFace - find matching faces in an image database

Object detection
1.0.0
MIT
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Lightweight production-ready face analysis library built on ONNX Runtime.

Object detection
1.0.0
MIT
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Inference for Segment Anything Model 3 (SAM3) - Interactive instance segmentation.

Semantic segmentation
1.0.0
Custom license
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Train DEIMv2: Real-Time Object Detection Meets DINOv3

Object detection
1.1.0
Apache-2.0
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Infer DEIMv2: Real-Time Object Detection Meets DINOv3

Object detection
1.1.0
Apache-2.0
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Run vision-language model series based on Qwen3

VLM
1.1.0
Apache-2.0
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Run vision-language model series based on Qwen2.5

VLM
1.2.0
Apache-2.0
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Train RF-DETR models

Object detection
1.2.0
Apache-2.0
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Inference with RF-DETR models

Object detection
1.2.0
Apache-2.0
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OCR-free model for document understanding

OCR
1.2.0
MIT
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Train YOLO11 classification models.

Classification
1.2.0
AGPL-3.0
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Inference with YOLOv11 image classification models

Classification
1.2.0
AGPL-3.0
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Inference with YOLOv11 pose estimation models

Keypoints detection
1.2.0
AGPL-3.0
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Train YOLOv11 instance segmentation models.

Instance segmentation
1.2.0
AGPL-3.0
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Inference with YOLOv11 segmentation models

Instance segmentation
1.2.0
AGPL-3.0
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Train YOLOv11 object detection models.

Object detection
1.2.0
AGPL-3.0
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Inference with YOLOv11 models

Object detection
1.2.0
AGPL-3.0
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Remove background with rembg library

Image matting
1.3.0
MIT
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Depth Anything V2 is a highly practical solution for robust monocular depth estimation

1.2.0
Apache-2.0
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Inference for Segment Anything Model 2 (SAM2).

Semantic segmentation
1.3.0
Apache-2.0