clustering-runs-plugin
sahi
clustering-runs-plugin | sahi | |
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1 | 11 | |
4 | 3,656 | |
- | 4.3% | |
- | 7.4 | |
about 2 months ago | 10 days ago | |
Python | Python | |
- | MIT License |
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clustering-runs-plugin
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How to Cluster Images
The FiftyOne Clustering Plugin makes our lives even easier. It provides the connective tissue between scikit-learn’s clustering algorithms and our images and wraps all of this in a simple UI within the FiftyOne App. We can install the plugin from the CLI:
sahi
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How to Detect Small Objects
An alternative to this is to leverage existing object detection, apply the model to patches or slices of fixed size in our image, and then stitch the results together. This is the idea behind Slicing-Aided Hyper Inference!
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Small-Object Detection using YOLOv8
Hi All, I am trying to detect defects in the images using YOLOv8where some of the classes (defectType1, defectType2) have very small bounding boxes and some of them have large bounding boxes associated with the, (defectType3, defectType4). Also, real-time operation is desired (at least 5Hz on Jetson Xavier) What I have done till now: I am primarily trying to use the SAHI technique (Slicing Aided Hyper Inference)
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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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Which Azure service to host this ML model
I need to execute this model https://github.com/obss/sahi upon an HTTP request. I will need between 32GB and 128GB of RAM (depending on the request). Also, I will only receive this request once or twice a week (they are not predefined dates). Each process may take a few hours.
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Library for chopping image in pieces for training
https://github.com/obss/sahi should do the job
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Semantic Segmentation with 2048x1024 images
I think you have multiple options: why run inference on this large resolution? Why not run on 1024x512 or smaller. Use a smaller model which uses less memory, eg enet, erfnet, bisenet etc. Otherwise, patchbased inference is the way to go, there is a nice library, but also easy to implement yourself: https://github.com/obss/sahi
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How to convert big TIF image to smaller jpgs
i have the EXACT thing ! the libs github!
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Roboflow 100: A New Object Detection Benchmark
Good idea. I haven’t looked too closely yet at the “hard” datasets.
We originally considered “fixing” the labels on these datasets by hand, but ultimately decided that label error is one of the challenges “real world” datasets have that models should work to become more robust against. There is some selection bias in that we did make sure that the datasets we chose passed the eye test (in other words, it looked like the user spent a considerable amount of time annotating & a sample of the images looked like they labeled some object of interest).
For aerial images in particular my guess would be that these models suffer from the “small object problem”[1] where the subjects are tiny compared to the size of the image. Trying a sliding window based approach like SAHI[2] on them would probably produce much better results (at the expense of much lower inference speed).
[1] https://blog.roboflow.com/detect-small-objects/
[2] https://github.com/obss/sahi
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Diffusion model for synthetc data generation
I am not very experienced, but do I understand that the problem is the size of the image? If so, have you heard of sahi
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Which model is best for detecting small objects? Yolov3? MaskRCNN, Faster-RCNN?
Try slicing and yolov4. https://github.com/obss/sahi
What are some alternatives?
mmdetection - OpenMMLab Detection Toolbox and Benchmark
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
awesome-tiny-object-detection - 🕶 A curated list of Tiny Object Detection papers and related resources.
fastdup - fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale.
datumaro - Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
tensorRT_Pro - C++ library based on tensorrt integration
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
3d-multi-resolution-rcnn - Official PyTorch implementaiton of the paper "3D Instance Segmentation Framework for Cerebral Microbleeds using 3D Multi-Resolution R-CNN."
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)