mmdetection
detectron2
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mmdetection | detectron2 | |
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23 | 48 | |
27,433 | 28,380 | |
1.9% | 1.6% | |
8.7 | 7.5 | |
9 days ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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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[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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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
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I trained a neural net to watch Super Smash Bros
I used the PointRend implementation from the mmdet project https://github.com/open-mmlab/mmdetection
- bike classification advice sought for used bike aggregator
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I want to create a pill counter using points instead of bounding boxes. What model should I train from?
If you are really that lazy: use bboxes of the fixed size placed in the center of the pill. The pill does not have to fit into the box - modern architectures see the image as a whole, not only the crop in the box. For example if you would train detection on labels which are shifted (add 30px to each label coordinate), the network would learn to place each box 30px next to the actual object. So just let small box represent the center of the pill. The problem will arise if you will use improperly configured architecture, i.e. if you will not change the anchors in SSD model. Try efficientdet architecture implemented in mmdetection or, the easiest, yolov5. These should work out of the box.
detectron2
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Instance segmentation of small objects in grainy drone imagery
And not enough true positives either. Add more augmentations in the config. Also make sure the config is set correctly, so that Detectron2 isn't skipping background images: https://github.com/facebookresearch/detectron2/issues/80
Similarly, I am using the more simple R50-FPN backbone (https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md) which may be too simple for grainy, small object, segmentation tasks
Thank you, I will have a look at it. I'm not that knowledgeable about existing models. Detectron2 also provides different different backbones (https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md). Is there a reason you recommend the segmentation-models library (apologies for the naive question)?
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AI Real Time (lgd for cn)
Which is built on https://github.com/facebookresearch/detectron2
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List of AI-Models
Click to Learn more...
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good computer vision or deep learning projects in github
Detectron2 (GitHub: https://github.com/facebookresearch/detectron2) is a Facebook AI Research library with state-of-the-art object detection and segmentation algorithms in PyTorch.
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Object Detection using PyTorch: Would you recommend a Framework (Detectron2, MMDetection, ...) or a project from scratch ?
That would be awesome. But I think that Detectron only provides RCNN, but I could be wrong. At least the model zoo on github looks like it.
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PyTorch 2.0 Release
I could fine-tune a Detectron2 model a few months ago using PyTorch and MPS backend [1]. I'd be interested if it's working yet.
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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.
What are some alternatives?
yolov5 - YOLOv5 π in PyTorch > ONNX > CoreML > TFLite
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
U-2-Net - The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
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]
rembg - Rembg is a tool to remove images background
deep-text-recognition-benchmark - Text recognition (optical character recognition) with deep learning methods, ICCV 2019
car-damage-detection - Detectron2 for car damage detection using custom dataset
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots