deep learning for image processing including classification and object-detection etc.
-
Updated
Jun 5, 2024 - Python
deep learning for image processing including classification and object-detection etc.
Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
Semantic segmentation models with pretrained convolutional and transformer-based backbones. PyTorch.
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Pytorch implementation of convolutional neural network visualization techniques
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Mask RCNN in TensorFlow
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
PaddlePaddle End-to-End Development Toolkit(飞桨低代码开发工具)
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
🛠 A lite C++ toolkit of awesome AI models, support ONNXRuntime, MNN. Contains YOLOv5, YOLOv6, YOLOX, YOLOR, FaceDet, HeadSeg, HeadPose, Matting etc. Engine: ONNXRuntime, MNN.
Collection of papers, datasets, code and other resources for object tracking and detection using deep learning
Add a description, image, and links to the segmentation topic page so that developers can more easily learn about it.
To associate your repository with the segmentation topic, visit your repo's landing page and select "manage topics."