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anomaly-detection-patchsvdd-pytorch

bridge_crack_image_data

The data set is used to train the DBCC model and detect bridge cracks

cac

caffe

Caffe fork that supports CRF-RNN

cam

Class Activation Mapping

crackforest-dataset

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-svdd-pytorch

A PyTorch implementation of the Deep SVDD anomaly detection method

deeplab_v3

Implementation of the Semantic Segmentation DeepLab_V3 CNN as described in: https://arxiv.org/pdf/1606.00915.pdf

deepsegmentor

A Pytorch implementation of DeepCrack and RoadNet projects.

ganomaly

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

groupvit

Official PyTorch implementation of GroupViT: Semantic Segmentation Emerges from Text Supervision, CVPR 2022.

kittiseg

A Kitti Road Segmentation model implemented in tensorflow.

l_dmi

Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"

metacorrection

[CVPR'21] MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

moby

Moby Project - a collaborative project for the container ecosystem to assemble container-based systems

models

Models and examples built with TensorFlow

modern-cpp-tutorial

📚 高速上手现代 C++ 11/14/17

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

tensorflow-fcn

An Implementation of Fully Convolutional Networks in Tensorflow.

tensorflow-resnet

ResNet model in TensorFlow

tensorflow-vgg

VGG19 and VGG16 on Tensorflow

test

unet

unet for image segmentation

weaklysupervisedcrackseg

wechat_jump_game

python 微信《跳一跳》辅助

winclip

[CVPR 2023] Unofficial re-implementation of "WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation".