super-resolution
MIRNet-TFJS
| super-resolution | MIRNet-TFJS | |
|---|---|---|
| 2 | 7 | |
| 1,452 | 347 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | about 1 year ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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super-resolution
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Is it not possible to get good results in Deep Learning if dataset is small (1000 images)? [D]
https://github.com/krasserm/super-resolution (Super useful for me)
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How to train "models" for opensv edsr (iam super noob)
And thanks ! so it means that I need to train it with one of these trainers inside this project for example https://github.com/krasserm/super-resolution And that means that iam forced to use python (at least for training) but it's OK 🙃
MIRNet-TFJS
What are some alternatives?
Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).
TrainYourOwnYOLO - Train a state-of-the-art yolov3 object detector from scratch!
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
TFServing-Demos - TF Serving demos
Hands-On-Meta-Learning-With-Python - Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
FSRCNN-pytorch - PyTorch implementation of Accelerating the Super-Resolution Convolutional Neural Network (ECCV 2016)
License-super-resolution - A License Plate Image Reconstruction Project in Tensorflow2
yolov7-tfjs - Object Detection using Yolov7 in tensorflow.js
SinGAN - Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0