truskovskiyk / Nima.pytorch
Licence: mit
NIMA: Neural IMage Assessment
Stars: ✭ 227
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Nima.pytorch
Cnn Paper2
🎨 🎨 深度学习 卷积神经网络教程 :图像识别,目标检测,语义分割,实例分割,人脸识别,神经风格转换,GAN等🎨🎨 https://dataxujing.github.io/CNN-paper2/
Stars: ✭ 77 (-66.08%)
Mutual labels: arxiv
Arxivscraper
A python module to scrape arxiv.org for specific date range and categories
Stars: ✭ 121 (-46.7%)
Mutual labels: arxiv
Arxiv Latex Cleaner
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
Stars: ✭ 2,689 (+1084.58%)
Mutual labels: arxiv
Lipreading Densenet3d
DenseNet3D Model In "LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild", https://arxiv.org/abs/1810.06990
Stars: ✭ 91 (-59.91%)
Mutual labels: arxiv
Mff Pytorch
Motion Fused Frames implementation in PyTorch, codes and pretrained models.
Stars: ✭ 116 (-48.9%)
Mutual labels: arxiv
Document Classifier Lstm
A bidirectional LSTM with attention for multiclass/multilabel text classification.
Stars: ✭ 136 (-40.09%)
Mutual labels: arxiv
Casual Digressions
💤 Old repository of notes on machine learning papers.
Stars: ✭ 73 (-67.84%)
Mutual labels: arxiv
Research Paper Notes
Notes and Summaries on ML-related Research Papers (with optional implementations)
Stars: ✭ 218 (-3.96%)
Mutual labels: arxiv
Scihub2pdf
Downloads pdfs via a DOI number, article title or a bibtex file, using the database of libgen(sci-hub) , arxiv
Stars: ✭ 120 (-47.14%)
Mutual labels: arxiv
Paper2remarkable
Fetch an academic paper or web article and send it to the reMarkable tablet with a single command
Stars: ✭ 177 (-22.03%)
Mutual labels: arxiv
Reproducible Image Denoising State Of The Art
Collection of popular and reproducible image denoising works.
Stars: ✭ 1,776 (+682.38%)
Mutual labels: arxiv
Paper Survey
📚Survey of previous research and related works on machine learning (especially Deep Learning) in Japanese
Stars: ✭ 140 (-38.33%)
Mutual labels: arxiv
Awesome Vln
A curated list of research papers in Vision-Language Navigation (VLN)
Stars: ✭ 86 (-62.11%)
Mutual labels: arxiv
Arxiv Collector
A little Python script to collect LaTeX sources for upload to the arXiv.
Stars: ✭ 179 (-21.15%)
Mutual labels: arxiv
Arxiv Vanity
Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.
Stars: ✭ 1,190 (+424.23%)
Mutual labels: arxiv
Bibcure
Bibcure helps in boring tasks by keeping your bibfile up to date and normalized...also allows you to easily download all papers inside your bibtex
Stars: ✭ 124 (-45.37%)
Mutual labels: arxiv
Triplet Attention
Official PyTorch Implementation for "Rotate to Attend: Convolutional Triplet Attention Module." [WACV 2021]
Stars: ✭ 222 (-2.2%)
Mutual labels: arxiv
Bayesiandeeplearning Survey
Bayesian Deep Learning: A Survey
Stars: ✭ 214 (-5.73%)
Mutual labels: arxiv
Coreference Resolution
Efficient and clean PyTorch reimplementation of "End-to-end Neural Coreference Resolution" (Lee et al., EMNLP 2017).
Stars: ✭ 144 (-36.56%)
Mutual labels: arxiv
PyTorch NIMA: Neural IMage Assessment
PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from this post at Google Research Blog.
Installing
Docker
docker run -it truskovskiyk/nima:latest /bin/bash
PYPI package (In Progress)
pip install nima
VirtualEnv
git clone https://github.com/truskovskiyk/nima.pytorch.git
cd nima.pytorch
virtualenv -p python3.7 env
source ./env/bin/activate
Dataset
The model was trained on the AVA (Aesthetic Visual Analysis) dataset
You can get it from here
Here are some examples of images with theire scores
Pre-train model (In Progress)
Deployment (In progress)
Usage
nima-cli
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
get_image_score Get image scores
prepare_dataset Parse, clean and split dataset
run_web_api Start server for model serving
train_model Train model
validate_model Validate model
Previous version of this project is still valid and works
Contributing
Contributing are welcome
License
This project is licensed under the MIT License - see the LICENSE file for details
Acknowledgments
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].