opencv
simple-faster-rcnn-pytorch
| opencv | simple-faster-rcnn-pytorch | |
|---|---|---|
| 1 | 1 | |
| 30 | 3,891 | |
| - | - | |
| 0.0 | 0.0 | |
| about 3 years ago | almost 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 or later |
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opencv
simple-faster-rcnn-pytorch
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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?
What are some alternatives?
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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.
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.
automl - Google Brain AutoML
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