HugsVision VS Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning

Compare HugsVision vs Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning and see what are their differences.

HugsVision

HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision (by qanastek)
huggingface Transformers Computer Vision pretrained-models image-classification semantic-segmentation object-detection image-generation pythorch pytorch-transformers Yolo vit deit detr Pytorch Deep Learning Machine Learning state-of-the-art Bert torchvision
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pypi.org
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Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning

My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset. (by alen-smajic)
computer-science Computer Vision Machine Learning Deep Learning Python jupyter-notebook Pytorch Numpy Cnn object-detection Yolo faster-rcnn berkeley-deep-drive bdd100k
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HugsVision Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
1 8
188 57
- -
0.0 3.6
9 months ago about 3 years ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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HugsVision

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

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning

Posts with mentions or reviews of Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing HugsVision and Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning you can also consider the following projects:

poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)

get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

fashionpedia-api - Python API for Fashionpedia Dataset

yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x

ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]

simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper

CoordConv

lama - 🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

NYU-DLSP20 - NYU Deep Learning Spring 2020

Vision-Project-Image-Segmentation

Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)

HugsVision vs poolformer Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning vs get-started-with-JAX HugsVision vs fashionpedia-api Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning vs yolo-tf2 HugsVision vs ganspace Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning vs simple-faster-rcnn-pytorch HugsVision vs CoordConv Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning vs lama HugsVision vs Transformer-Explainability Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning vs NYU-DLSP20 HugsVision vs Vision-Project-Image-Segmentation Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning vs Mask-RCNN-Implementation