efficientdet-pytorch
mmsegmentation
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efficientdet-pytorch | mmsegmentation | |
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1 | 7 | |
1,550 | 7,414 | |
- | 4.3% | |
4.1 | 8.2 | |
9 months ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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efficientdet-pytorch
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Bounding box annotations and object orientation
However, there are papers on oriented object detectors (see https://arxiv.org/pdf/1911.07732.pdf) for example. In that paper, they do achieve better results using oriented bounding boxes. If you want to go down that route, I would suggest using the EfficientDet model, because the PyTorch code that you'll find for it is quite easy to understand and modify. For example, I've taken https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch, and modified it to include a "thing-ness" logit, and this was pretty easy to do. Classic EfficientDet models only include logits (aka output neurons that get softmax-ed) for each class, and if any one of these class neurons is greater than 0.5, then it is considered "a thing". Anyway - that's digression, but my point is that I've thought about adding oriented box support to an EfficientDet model, and it didn't seem to be too hard, although I haven't actually done it. If I was to start now, I would probably go with https://github.com/rwightman/efficientdet-pytorch, since Ross Wightman's models are becoming a de-facto standard in the PyTorch world for all things image-related.
mmsegmentation
- [D] The MMSegmentation library from OpenMMLab appears to return the wrong results when computing basic image segmentation metrics such as the Jaccard index (IoU - intersection-over-union). It appears to compute recall (sensitivity) instead of IoU, which artificially inflates the performance metrics.
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Is there any ML model out there for room surfaces detection? (ceiling, floor, windows)
Segmentation models trained on datasets like ADE20k could probably be used for that, because it has separate classes for these things iirc. https://github.com/open-mmlab/mmsegmentation should have suitable pretrained models available.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- Mmsegmentation - Openmmlab semantic segmentation toolbox and benchmark.
- Mmsegmentation – Openmmlab semantic segmentation toolbox and benchmark
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Semantic Segmentation models
This repo is amazing: https://github.com/open-mmlab/mmsegmentation
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What's A Simple Custom Segmentation Pipeline?
Mmsegmentation would be a good place to start for basic segmentation. They have lots of recent methods and pretained models you could fine-tune from. They also support quite a few datasets including VOC. There is a custom dataset format which looks straightforward to create.
What are some alternatives?
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
Yet-Another-EfficientDet-Pytorch - The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
involution - [CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
ros-semantic-segmentation-pytorch - Pytorch implementation of Semantic Segmentation in ROS on MIT ADE20K dataset based on semantic-segmentation-pytorch by CSAIL
PaddleSeg - Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.