merged_depth
mildlyoverfitted
merged_depth | mildlyoverfitted | |
---|---|---|
3 | 1 | |
45 | 323 | |
- | - | |
1.8 | 6.0 | |
over 2 years ago | 4 months ago | |
Python | Python | |
MIT License | MIT License |
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.
merged_depth
- [P] Monocular Depth Estimation - I ran a number of fairly well-known pre-trained models and looked at the average
-
Monocular Depth Estimation - Running multiple pre-trained models and looking at the average
Project Link: https://github.com/p-ranav/merged_depth
- I ran 4 pre-trained depth estimation models and looked at the average
mildlyoverfitted
What are some alternatives?
AdaBins - Official implementation of Adabins: Depth Estimation using adaptive bins
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.
Cam-Hackers - Hack Cameras CCTV FREE
stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
magicavoxel-shaders - A collection of shaders for MagicaVoxel to generate geometry, noise, patterns, and simplify common and repetitive tasks.
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
Jetson-Nano-Ubuntu-20-image - Jetson Nano with Ubuntu 20.04 image
koila - Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.
Swin-Transformer-Serve - Deploy Swin Transformer using TorchServe
torchextractor - Feature extraction made simple with torchextractor
torchgeo - TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
torchinfo - View model summaries in PyTorch!