MeteorsHub's starred repositories
awesome-mac
Now we have become very big, Different from the original idea. Collect premium software in various categories.
ml-stable-diffusion
Stable Diffusion with Core ML on Apple Silicon
Grounded-Segment-Anything
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
segmentation_models.pytorch
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
NetNewsWire
RSS reader for macOS and iOS.
EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
TNN
TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.
Awesome-Incremental-Learning
Awesome Incremental Learning
MedicalNet
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.
hiddenlayer
Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.
awesome-transformers-in-medical-imaging
A collection of resources on applications of Transformers in Medical Imaging.
batchgenerators
A framework for data augmentation for 2D and 3D image classification and segmentation
GVV-Differentiable-CUDA-Renderer
Differentiable Rasterization-based Renderer implemented in CUDA and C++
One_Click_Meteor_Shower
Initial version