dnn_from_scratch VS the-gan-zoo

Compare dnn_from_scratch vs the-gan-zoo and see what are their differences.

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dnn_from_scratch the-gan-zoo
1 2
29 14,007
- -
0.0 0.0
almost 3 years ago 7 months ago
Python Python
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

dnn_from_scratch

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

the-gan-zoo

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

What are some alternatives?

When comparing dnn_from_scratch and the-gan-zoo you can also consider the following projects:

deepxde - A library for scientific machine learning and physics-informed learning

anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

HyperGAN - Composable GAN framework with api and user interface

Conditional-Generative-Adversarial-Network - Train a GAN to transform images from one domain to another domain

ALAE - [CVPR2020] Adversarial Latent Autoencoders

Anime-Generation - 🎨 Anime generation with GANs.

guesslang - Detect the programming language of a source code

stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

open-lpr - Open Source and Free License Plate Recognition Software

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0