mmdetection
yolov5
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mmdetection | yolov5 | |
---|---|---|
23 | 129 | |
27,742 | 46,738 | |
2.3% | 2.9% | |
8.7 | 8.9 | |
7 days ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
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 the 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
yolov5
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จำแนกสายพันธ์ุหมากับแมวง่ายๆด้วยYoLoV5
Ref https://www.youtube.com/watch?v=0GwnxFNfZhM https://github.com/ultralytics/yolov5 https://dev.to/gfstealer666/kaaraich-yolo-alkrithuemainkaartrwcchcchabwatthu-object-detection-3lef https://www.kaggle.com/datasets/devdgohil/the-oxfordiiit-pet-dataset/data
- How would i go about having YOLO v5 return me a list from left to right of all detected objects in an image?
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Building a Drowsiness Detection Web App from scratch - pt2
!git clone https://github.com/ultralytics/yolov5.git ## Navigate to the model %cd yolov5/ ## Install requirements !pip install -r requirements.txt ## Download the YOLOv5 model !wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt
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[Help: Project] Transfer Learning on YOLOv8
Specifically what I did was take the coco128.yaml, added 6 new classes from Dataset A (which have already been converted to YOLO Darknet TXT), from index 0-5 and subsequently adjusted the indices of the other COCO classes. The I proceeded to train and validate on Dataset A for 20 epochs.
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Changing labels of default YOLOv5 model
I am using the default YOLOv5m6 model here with sahi/yolov5 library for my object detection project. I want to change just some of labels - for example when YOLO detects a human, I want it to label the human as "threat", not "person". Is there any way I can do it just changing some code, or I should train the model from scratch by just changing labels?
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First time working with computer vision, need help figuring out a problem in my model
You should add them without annotations. Go through this.
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AI Camera?
You are correct and if you check the firmware, it's yet another famous 3rd party project without attribution, namely https://github.com/ultralytics/yolov5
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First non-default print on K1 - success
On one side, being a Linux user for 24 years now, it annoys me that they rip off code and claiming it as theirs again, thus violating licenses, but on the other thanks to k3d's exploit I'm able to tinker more with the machine and if needed do (selective) updates by hand then with a closed source system. It's not just "klipper", with klipper, fluidd and moonraker, it's also ffmpeg and mjpegstreamer. It's gonna be interesting since they also use a project that isn't just GPL, but APGL (in short "If your software gives service online, you have to publish the source code of it and any library that it borrows functions from.") - they use yolov5 (for AI).
- How does the background class work in object detection?
What are some alternatives?
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
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]
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
OpenCV - Open Source Computer Vision Library
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
yolov5-crowdhuman - Head and Person detection using yolov5. Detection from crowd.