deep learning for image processing including classification and object-detection etc.
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Updated
Jan 12, 2025 - Python
deep learning for image processing including classification and object-detection etc.
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
Pytorch implementation of convolutional neural network visualization techniques
BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
All-in-One Development Tool based on PaddlePaddle(飞桨低代码开发工具)
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
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 Python package for segmenting geospatial data with the Segment Anything Model (SAM)
Mask RCNN in TensorFlow
Sandbox for training deep learning networks
A procedural Blender pipeline for photorealistic training image generation
A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.
Papers and Datasets about Point Cloud.
Efficient vision foundation models for high-resolution generation and perception.
Pytorch framework for doing deep learning on point clouds.
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