DeepLearning VS vision_models_playground

Compare DeepLearning vs vision_models_playground and see what are their differences.

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DeepLearning vision_models_playground
1 1
3 12
- -
0.0 8.3
almost 2 years ago 6 months ago
Jupyter Notebook Jupyter Notebook
MIT License 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.

DeepLearning

Posts with mentions or reviews of DeepLearning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-06.

vision_models_playground

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

What are some alternatives?

When comparing DeepLearning and vision_models_playground you can also consider the following projects:

AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!

swarms - Orchestrate Swarms of Agents From Any Framework Like OpenAI, Langchain, and Etc for Real World Workflow Automation. Join our Community: https://discord.gg/DbjBMJTSWD

cs231n - Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition

Fast-Transformer - An implementation of Fastformer: Additive Attention Can Be All You Need, a Transformer Variant in TensorFlow

conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico

Transformer-in-Transformer - An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches

weightless_NN_decompression - Proof of concept for neural network decompression without storing any weights

CenterSnap - Pytorch code for ICRA'22 paper: "Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation"

Deep-Learning-Experiments - Videos, notes and experiments to understand deep learning

monodepth2 - [ICCV 2019] Monocular depth estimation from a single image