Ready to use Computer Vision algorithms and Apps.
Train DEIMv2: Real-Time Object Detection Meets DINOv3
Infer DEIMv2: Real-Time Object Detection Meets DINOv3
Train RF-DETR models
Inference with RF-DETR models
Train YOLOv11 object detection models.
Inference with YOLOv11 models
Run OWLv2 a zero-shot text-conditioned object detection model
Train YOLOv9 models
Object detection with YOLOv9 models
Inference of the Grounding DINO model
Train YOLOv8 object detection models.
Face detection using the Kornia API
Auto-annotate images with GroundingDINO and SAM models
Load COCO 2017 dataset
Load YOLO dataset
Inference with YOLOv8 models
Train for MMLAB detection models
Inference for MMDET from MMLAB detection models
TridentNet inference model of Detectron2 for object detection.
RetinaNet inference model of Detectron2 for object detection.
Inference for Detectron2 detection models
Panoptic driving Perception using YoloPv2
Train D-FINE models
Inference with D-FINE models
Run inference with YOLOv10 models
YOLOv7 object detection models.
Run florence 2 object detection with or without text prompt
Train YOLOv10 object detection models.
Face detection using Google cloud vision API.
Train for Detectron2 detection models
