fasterrcnn-pytorch-training-pipeline
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
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| fasterrcnn-pytorch-training-pipeline | Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning | |
|---|---|---|
| 11 | 8 | |
| 169 | 57 | |
| - | - | |
| 6.0 | 3.6 | |
| 1 day ago | about 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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fasterrcnn-pytorch-training-pipeline
- A simple library to train more than 20 Faster RCNN models using PyTorch (including ViTDet)
- A Library of Faster RCNN Models with Simple Training Pipeline for Custom Dataset
- PyTorch Faster RCNN Library - Support for transformer detection models.
- PyTorch Faster RCNN Custom Dataset Training Made Easy
- An efficient, powerful, and easy training pipeline for Faster RCNN models in PyTorch
- A Faster RCNN Object Detection Pipeline for Custom Training in PyTorch
- A PyTorch library for easily training Faster RCNN models (even with custom backbones) on custom datasets for object detection.
- A very simple pipeline to train FasterRCNN Object Detection Models (WRITTEN IN PYTORCH)
- A Faster RCNN Object Detection Pipeline for custom datasets using PyTorch - Get started with training in 5 minutes
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
- 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)
What are some alternatives?
simple-faster-rcnn-pytorch - A simplified implemention of Faster R-CNN that replicate performance from origin paper
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.
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
notebooks - Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
roboflow-100-benchmark - Code for replicating Roboflow 100 benchmark results and programmatically downloading benchmark datasets
sports - Cool experiments at the intersection of Computer Vision and Sports ⚽🏃
lama - 🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
NYU-DLSP20 - NYU Deep Learning Spring 2020
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)