simple-faster-rcnn-pytorch
opencv
Our great sponsors
| simple-faster-rcnn-pytorch | opencv | |
|---|---|---|
| 1 | 1 | |
| 3,891 | 30 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 3 years ago | about 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | 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.
simple-faster-rcnn-pytorch
-
ISO Easy to Modify and Use Faster RCNN PyTorch Implementation
Hi all, as the title suggests I'm looking for a GitHub repo where I can edit a Faster RCNN implementation rather easily. I'm basically looking to test an idea where I have multiple branches with feature map and bounding boxes as inputs. I've modified the built-in torchvision implementation once before, but I think it's a little more complicated than I like, and I'd rather not release the entire torchvision package as part of my own work in the future. I have looked briefly into this repo https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/master/trainer.py but it only supports a batch size of 1, and I'm not sure what it'd take to expand that capability. Is there anything better out there?
opencv
What are some alternatives?
Yet-Another-EfficientDet-Pytorch - The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
lane-detection-opencv - This is for detecting any lane in the road to identify the road map
mmdetection - OpenMMLab Detection Toolbox and Benchmark
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.
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.
OwnPhotos - Self hosted alternative to Google Photos
automl - Google Brain AutoML
iterative-grabcut - This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
fasterrcnn-pytorch-training-pipeline - PyTorch Faster R-CNN Object Detection on Custom Dataset
mobilenets-ssd-pytorch - MobileNets-SSD/SSDLite on VOC/BDD100K Datasets