The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning Alternatives
Similar projects and alternatives to Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
-
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.
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
simple-faster-rcnn-pytorch
1 Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS simple-faster-rcnn-pytorchA simplified implemention of Faster R-CNN that replicate performance from origin paper
-
lama
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
gan-vae-pretrained-pytorch
1 Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS gan-vae-pretrained-pytorchPretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning reviews and mentions
- Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN based on the BDD100K dataset
- [P] Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN on the BDD100K dataset, Goethe University Frankfurt Germany (Fall 2020)
- Real-time Object Detection for Autonomous Driving using Deep Learning, Performance comparison of YOLO and Faster R-CNN on the BDD100K dataset, Goethe University Frankfurt Germany (Fall 2020)
- Real-time Object Detection for Autonomous Driving using Deep Learning, Goethe University Frankfurt Germany (Fall 2020)
-
A note from our sponsor - WorkOS
workos.com | 25 Apr 2024
Stats
alen-smajic/Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning is Jupyter Notebook.
Popular Comparisons
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS get-started-with-JAX
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS yolo-tf2
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS HugsVision
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS simple-faster-rcnn-pytorch
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS lama
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS NYU-DLSP20
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS Mask-RCNN-Implementation
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS dl-colab-notebooks
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS fashionpedia-api
- Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning VS gan-vae-pretrained-pytorch
Sponsored