mmdetection
CenterNet
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mmdetection | CenterNet | |
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23 | 6 | |
27,658 | 7,101 | |
2.0% | - | |
8.7 | 0.0 | |
5 days ago | about 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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mmdetection
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Semantic segementation
When I look for benchmarks I always start here https://paperswithcode.com/task/instance-segmentation/codeless it has the lists of datasets to measure models accross lots o papers. Many are very specific models with low support or community but it gives you a good idea of ββthe state of the art. It also lists repositories related to good community. https://github.com/open-mmlab/mmdetection seems very active and the one that is being used the most, you could use the models that it has integrated in its model zoo, within the same repository. It has the benchmarks to compare those same models and some of them are from 2022
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How to Convert Model Mask into Polygon and save JSON?
MODEL: https://github.com/open-mmlab/mmdetection
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Object Detection Model for Custom Dataset Training?
Would it make sense to work with OpenMMLab (https://github.com/open-mmlab/mmdetection) or Pytorch-image-models (https://github.com/rwightman/pytorch-image-models#models) since they offer a variety of models?
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[P] Image search with localization and open-vocabulary reranking.
I wanted to have a few choices getting localization into image search (index and search time). I immediately thought of using a region proposal network (rpn) from mask-rcnn to create patches that can also be indexed and searched (and add the localisation). I figured it might be somewhat agnostic to classes. I did not want to use mmdetection or detectron2 due to their dependencies and just getting the rpn was not worth it. I was encouraged by the PyTorch native implementations of detection/segmentation models but ended up finding yolox the best.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMDetection: OpenMMLab detection toolbox and benchmark.
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Removing the bounding box generated by OnnxRuntime segmentation
I have a semantic segmentation model trained using the mmdetection repo. Then it is converted to the ONNX format using the mmdeploy repo.
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Keras vs Tensorflow vs Pytorch for a Final year Project
E.g. If you consider it an object detection problem it is: detect and localise all the pedestrians in a frame, and classify them by their (intended) action. IMO the easiest way to do this would be with mmdetection, which is built on top of pytorch. Just label your dataset, build a config, and boom you have a model. Inference with that model in only a few lines of code, you won't really need to learn too much to get started.
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DeepSort with PyTorch(support yolo series)
MMDetection
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[D] Pre-trained networks and batch normalization
For example, in mmdetection, they expose options in their config & implementation to freeze batch norm layers in backbones and in this config, norm_eval is set to True meaning to freeze tracking of batch norm stats, while the ResNet backbone is frozen up to the 1st stage. Example of their backbone implementation can be found here.
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Config files in plain Python
MMDetection uses config Python scripting. It's easier to define nn.Module objects other than writing class name in a json config file
CenterNet
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Seeking AI Model to Predict the Center of an Object in Images
CenterNet comes to mind
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[D] Cross Shape Artifact in Heatmap
Found relevant code at https://github.com/xingyizhou/CenterNet + all code implementations here
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CUDA errors while trying to run CenterNet
I am trying the implement this paper https://github.com/xingyizhou/CenterNet
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I want to create a pill counter using points instead of bounding boxes. What model should I train from?
Take a look at this centernet architecture.
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Using PyTorch and NumPy? You're making a mistake
Yeah, I'd run into this 2 years ago and ended up also reporting an issue on the Centernet repo [1]
The solution I have in that repo adapts from the very helpful discussions in the original Pytorch issue [2]
I will admit that this is *very* easy to mess up as evidenced by the fact that examples in the official tutorials for Pytorch and other well known code-bases suffer from it. In the Pytorch training framework I've developed at work, we've implemented a custom `worker_init_fn` as outlined in [1] that is the default for all "trainer" instances who are responsible for instantiating DataLoaders in 99% of our training runs.
- [P] Using PyTorch + NumPy? A bug that plagues thousands of open-source ML projects.
What are some alternatives?
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
yolov5 - YOLOv5 π in PyTorch > ONNX > CoreML > TFLite
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
Kornia - Geometric Computer Vision Library for Spatial AI
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
pose-tensorflow - Human Pose estimation with TensorFlow framework
mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.
tensorflow - An Open Source Machine Learning Framework for Everyone
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans