dino
mmdetection
dino | mmdetection | |
---|---|---|
7 | 23 | |
5,854 | 27,833 | |
1.4% | 1.4% | |
1.0 | 8.4 | |
24 days ago | 9 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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dino
- Batch-wise processing or image-by-image processing? (DINO V1)
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[P] Image search with localization and open-vocabulary reranking.
I also implemented one based on the self attention maps from the DINO trained ViT’s. This worked pretty well when the attention maps were combined with some traditional computer vision to get bounding boxes. It seemed an ok compromise between domain specialization and location specificity. I did not try any saliency or gradient based methods as i was not sure on generalization and speed respectively. I know LAVIS has an implementation of grad cam and it seems to work well in the plug'n'play vqa.
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Unsupervised semantic segmentation
You will probably need an unwieldy amount of data and compute to reproduce it, so your best option would be to use the pretrained models available on github.
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[D] Why Transformers are taking over the Compute Vision world: Self-Supervised Vision Transformers with DINO explained in 7 minutes!
[Full Explanation Post] [Arxiv] [Project Page]
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A major part of real-world AI has to be solved to make unsupervised, generalized full self-driving work, as the entire road system is designed for biological neural nets with optical imagers
Except he is actually talking about the new DINO model created by facebook that was released on friday. Which is a new approach to image transformers for unsupervised segmentation. Here's its github.
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[D] Paper Explained - DINO: Emerging Properties in Self-Supervised Vision Transformers (Full Video Analysis)
Code: https://github.com/facebookresearch/dino
- [R] DINO and PAWS: Advancing the state of the art in computer vision with self-supervised Transformers
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
What are some alternatives?
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Transformer-SSL - This is an official implementation for "Self-Supervised Learning with Swin Transformers".
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
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]
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.
lightly - A python library for self-supervised learning on images.
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots