S2ML-Generators VS ArtLine

Compare S2ML-Generators vs ArtLine and see what are their differences.

S2ML-Generators

Multiple notebooks which allow the use of various machine learning methods to generate or modify multimedia content (by somewheresy)
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S2ML-Generators ArtLine
3 12
176 3,531
- -
2.7 1.4
6 months ago about 1 year ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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S2ML-Generators

Posts with mentions or reviews of S2ML-Generators. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning S2ML-Generators yet.
Tracking mentions began in Dec 2020.

ArtLine

Posts with mentions or reviews of ArtLine. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-25.

What are some alternatives?

When comparing S2ML-Generators and ArtLine you can also consider the following projects:

S2ML-Art-Generator - Multiple notebooks which allow the use of various machine learning methods to generate or modify multimedia content [Moved to: https://github.com/justin-bennington/S2ML-Generators]

DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

Toon-Me - A Deep Learning project to Toon Portrait Images

U-2-Net - The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

APDrawingGAN - Code for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)

colab_openvino - OpenVINO如何安裝在Colab並執行mobilenet-V1影像分類及mobilenet-SSD物件偵測範例

clip-retrieval - Easily compute clip embeddings and build a clip retrieval system with them

rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]

vqgan-clip-generator - Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs