NN Image Labeling
Use deployed neural network in labeling interface
Use deployed neural network in labeling interface
Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training
NN Inference on images in project or dataset
Dashboard to configure and monitor training
Deploy model as REST API service
Deploy YOLOv8 | v9 | v10 | v11 as REST API service
Dashboard to configure, start and monitor training
Training dashboard for mmdetection framework (v3.0.0 and above).
Deploy model as REST API service
Visualize and build augmentation pipeline with ImgAug
Generate synthetic data: flying foregrounds on top of backgrounds
Class-agnostic interactive detection for auto-prelabeling
Deploy MMDetection 3.0 model as a REST API service
Class-angnostic object detection model
Interactive Confusion matrix, mAP, ROC and more
Evaluate the performance of the NN model and compare it with the results of other models
Label project images using detector and pose estimator
Calculate and visualize embeddings
Label project images using detector and classify predicted boxes
Apply NN models to video frames
App to obscure data on images and videos
to TorchScript and ONNX formats
Dashboard to configure, start and monitor YOLOv5 2.0 training
Train RT-DETR model on your data
Deploy YOLOv5 2.0 as REST API service
Train MMDetection3D for detection on Point Clouds data
Deploy MMDetection3D models to detect objects in Point Clouds
Train RT-DETRv2 model on your data
to TorchScript and ONNX formats
Text-Prompted Object Detection with Mask Segmentation
Apply pretrained models for underwater species detection
Deploy RT-DETR as a REST API service
Deploy Florence-2 as a REST API service
Deploy RT-DETRv2 as a REST API service
All you need to work with YOLOv5