mbutil
sahi
mbutil | sahi | |
---|---|---|
2 | 11 | |
720 | 3,580 | |
0.8% | 2.3% | |
1.8 | 7.4 | |
about 2 years ago | 4 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
mbutil
-
Map Tiling App -feedback requested - seven day trial version available
Then use Mapbox's own utility for parallel processing into your output: https://github.com/mapbox/mbutil
-
Serving open street map vector tiles with elixir and phoenix
There is also an alternative to just unpack the files and serve it form the /priv/static folder. To serve it this way we need mb-util app installed - The files are compressed, so we need either unpack them or rename to have .gz extension - then phoenix will serve it gzipped and add the header for us.
sahi
-
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!
-
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)
-
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?
-
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.
-
Library for chopping image in pieces for training
https://github.com/obss/sahi should do the job
-
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
-
How to convert big TIF image to smaller jpgs
i have the EXACT thing ! the libs github!
-
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
-
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
-
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?
mbtiles-spec - specification documents for the MBTiles tileset format
mmdetection - OpenMMLab Detection Toolbox and Benchmark
rio-toa - Top Of Atmosphere (TOA) calculations for Landsat 8
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
google-maps-at-88-mph - Google Maps keeps old satellite imagery around for a while – this tool collects what's available for a user-specified region in the form of a GIF.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
termtrack - Track satellites in your terminal
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