torch-conv-kan
This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based o
Detect and classify traffic signs in images using a machine learning model.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytimeThis project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based o
torch-conv-kan has 531 stars on GitHub. It has been forked 44 times. torch-conv-kan is written mainly in Python. It has been in active development since 2024. torch-conv-kan is available under the MIT license. Its main topics are computer-vision, convolutional-neural-networks, kolmogorov-arnold-networks.
What is torch-conv-kan?
This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based o
Is torch-conv-kan open source?
torch-conv-kan is an open-source project. It is released under the MIT license.
Is torch-conv-kan free?
Yes. torch-conv-kan is free and open source — you can use, modify and self-host it.
What license does torch-conv-kan use?
torch-conv-kan is available under the MIT license.
What language is torch-conv-kan written in?
torch-conv-kan is written mainly in Python.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/ivandrokin-torch-conv-kan.html)
Measured from GitHub topics shared by both projects, weighted by how rare each topic is.