sahi
fastdup
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sahi | fastdup | |
---|---|---|
11 | 18 | |
3,580 | 1,403 | |
4.7% | 3.6% | |
7.4 | 9.4 | |
1 day ago | 26 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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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.
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
fastdup
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Visualize your dataset using DINOv2 embedding
Visualizing your dataset (especially large ones) in a low-dimensional embedding space can tell you a lot about the patterns and clusters in your dataset.
We recently release a notebook showing how you can visualize your dataset using DINOv2 models by running it on your CPU.
Yes! No GPUs needed.
We used it to find clusters of similar images, duplicates, and outliers in a subset of the LAION dataset
Try it on your own dataset:
Colab notebook - https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/dinov2_notebook.ipynb
GitHub repo - https://github.com/visual-layer/fastdup
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[R][P] How to extract feature vectors of large datasets using DINOv2 on CPU
Run 1M images from the LAION dataset through the DINOv2 model and cluster the images using a free tool - fastdup.
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Computer Vision pre-trained model for finding how similar two photos of a room are
Another option could be fastdup (https://github.com/visual-layer/fastdup) which is probably most helpful for analysis type objectives.
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Find image duplicates and outliers – A free, scalable, efficient tool
I recently stumbled upon fastdup a tool that lets you gain insights from a large image/video collection.
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How can we match images in our database?
There is this fastdup framework which supposedly allows you to find duplicates and similar images. i haven't used it though
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Measure Images Similarity
I came across fastdup recently https://github.com/visual-layer/fastdup
- Dedup-ing LAION (60M duplicates) and ImageNet (1.2M duplicates) with fastdup
- [R] Dedup-ing LAION (60M duplicates) and ImageNet (1.2M duplicates) with fastdup
What are some alternatives?
mmdetection - OpenMMLab Detection Toolbox and Benchmark
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
dhash - Python library to calculate the difference hash (perceptual hash) for a given image, useful for detecting duplicates
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
CVPR2024-Papers-with-Code - CVPR 2024 论文和开源项目合集
awesome-tiny-object-detection - 🕶 A curated list of Tiny Object Detection papers and related resources.
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
datumaro - Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
plakakia - Python image tiling library for image processing, object detection, etc.