make-sense
sahi
make-sense | sahi | |
---|---|---|
7 | 11 | |
2,969 | 3,580 | |
- | 2.3% | |
2.4 | 7.4 | |
about 2 months ago | 6 days ago | |
TypeScript | Python | |
GNU General Public License v3.0 only | MIT License |
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make-sense
- Need help identifying a good open source data annotation tool
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Free instance segmentation annotation tool
Hi 👋🏻! I’m creator of https://makesense.ai. It supports Instance Segmentation. Take a look at the repo: https://github.com/SkalskiP/make-sense
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Data Labelling Software
I created tool called MakeSense: https://github.com/SkalskiP/make-sense it is completely free and open sourced on GH
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Roboflow 100: A New Object Detection Benchmark
Haven't heard of those two, but would be really awesome to see an integration. We have an open API[1] for just this reason: we really want to make it easy to use (and source) your data across all the different tools out there. We've recently launched integrations with other labeling[2] and AutoML[3] tools (and have integrations with the big-cloud AutoML tools as well[4]). We're hoping to have a bunch more integrations with other MLOps tools & platforms in 2023.
Re synthetic data specifically, we've written a couple of how-to guides for creating data from context augmentation[5], Unity Perception[6], and Stable Diffusion[7] & are talking to some others as well; it seems like a natural integration point (and someplace where we don't need to reinvent the wheel).
[1] https://docs.roboflow.com/rest-api
[2] https://github.com/SkalskiP/make-sense/pull/298
[3] https://github.com/ultralytics/yolov5/discussions/10425
[4] https://docs.roboflow.com/train/pro-third-party-training-int...
[5] https://blog.roboflow.com/how-to-create-a-synthetic-dataset-...
[6] https://blog.roboflow.com/unity-perception-synthetic-dataset...
[7] https://blog.roboflow.com/synthetic-data-with-stable-diffusi...
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[Project] Football Players Tracking with YOLOv5 + ByteTRACK
Two things that carried me the most are my blog https://medium.com/@skalskip - which gave me my first job in computer vision, and my open-source GitHub project: https://github.com/SkalskiP/make-sense - which gave me all my jobs since I created it.
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Hi everyone! I'm Piotr and for several years I have been developing a small open-source project for labeling photos - makesense.ai. I added a new feature this weekend. You can use YOLOv5 models to automatically annotate photos.
Link to GitHub project: https://github.com/SkalskiP/make-sense
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Tool for human pose estimation keypoint annotation
I have also looked into make-sense and currently the docker and the npm refuse to work. I have already opened a ticket describing the issue .
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?
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
mmdetection - OpenMMLab Detection Toolbox and Benchmark
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/cvat-ai/cvat]
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
AID - One-Stop System for Machine Learning.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
Universal Data Tool - Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
awesome-tiny-object-detection - 🕶 A curated list of Tiny Object Detection papers and related resources.
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
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.