PyTorch-Vision-Transformer-ViT-MNIST-CIFAR10
SwinIR
| PyTorch-Vision-Transformer-ViT-MNIST-CIFAR10 | SwinIR | |
|---|---|---|
| 2 | 28 | |
| 65 | 4,102 | |
| - | - | |
| 8.8 | 0.0 | |
| 15 days ago | about 1 month ago | |
| Python | Python | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PyTorch-Vision-Transformer-ViT-MNIST-CIFAR10
-
Scratch Implementation of Vision Transformer in PyTorch
In the encoder class, ViTs use prenorm not post-norm like BERT. That is the first norm layer should be before the attention and the second norm layer should be before "self.fc1" https://github.com/s-chh/PyTorch-Vision-Transformer-ViT-MNIST/blob/main/model.py
SwinIR
- A smooth and sharp image interpolation you probably haven't heard of
- Certain directories (e.g. SwinIR) are empty (version: Empire Media Science A1111 Web UI Installer)
-
I used Real-ESRGAN to upscale my image, but if you zoom in you can see that “water particles” looks like some random lines and image overall looks cartoonish. Is there a way to fix it?
003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth
-
Any luck changing the upscaler? They seem to be hard coded
I was trying to get a new upscaler working, as someone pointed me to one that did a good job of preserving and creating new details: https://github.com/JingyunLiang/SwinIR
-
Spatial-temporal denoising
SwinIR: https://github.com/JingyunLiang/SwinIR
-
A Monster Hunter: World Virtual Photography Tutorial - YouTube
Upscalers that I use SwinIR https://github.com/JingyunLiang/SwinIR https://github.com/AUTOMATIC1111/stable-diffusion-webui (Use 'extras' tab for the upscaler function) Topaz Gigapixel AI https://www.topazlabs.com/gigapixel-ai
-
what are the alternatives to letsenhance.io?
You could try out chaiNNer, it is a free local/offline application. There are a lot of (upscaling) models which you can download an use with it. You can for example try out SwinIR-L (link will start a model download) or any other model you like depending on your input images.
-
[R] Swin transformer while using a rectangular attention window
the relative attention bias can be made non-square in the original implementation, there is a parameter window_size, at 7, that is forced to (7,7) directly, but you can change it easily. https://github.com/JingyunLiang/SwinIR/blob/main/models/network_swinir.py
-
Robot dance animation with Robo-Diffusion (1024x576)
Use SwinIR medium model to upscale by 2 times. This will result in a video of 2048x1152.
- Help Need to get my VQGAN images to 10000 x 10000
What are some alternatives?
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
PaddleViT - :robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Efficient-AI-Backbones - Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
Real-ESRGAN-ncnn-vulkan - NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
LaTeX-OCR - pix2tex: Using a ViT to convert images of equations into LaTeX code.
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
MPRNet - [CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.