mildlyoverfitted VS vision_transformer

Compare mildlyoverfitted vs vision_transformer and see what are their differences.

mildlyoverfitted

Paper implementations from scratch and machine learning tutorials (by jankrepl)

vision_transformer

Discover how to build vision transformer from scratch with this comprehensive tutorial. Follow our step-by-step guide to create your own vision transformer. (by MikhailKravets)
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mildlyoverfitted vision_transformer
1 1
323 9
- -
6.0 5.1
4 months ago about 1 year 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.

mildlyoverfitted

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

vision_transformer

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

What are some alternatives?

When comparing mildlyoverfitted and vision_transformer you can also consider the following projects:

merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models

a-PyTorch-Tutorial-to-Object-Detection - SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection

stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

PyTorchZeroToAll - Simple PyTorch Tutorials Zero to ALL!

torch-hypernetwork-tutorials - Hypernetwork training considerations and implementation types in PyTorch. Includes classification and time-series examples alongside 1D GroupConv Parallelization.