segment-geospatial
caer
| segment-geospatial | caer | |
|---|---|---|
| 3 | 8 | |
| 2,660 | 749 | |
| 1.3% | - | |
| 8.6 | 0.0 | |
| 10 days ago | 7 months ago | |
| Python | Python | |
| MIT License | MIT License |
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segment-geospatial
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Anyone using segment-geospatial?
Maybe try reporting as a GitHub issue? https://github.com/opengeos/segment-geospatial/issues
- How to I productionize my Instance Segmentation model ?
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Segment Anything & QGIS?
Update: Qiusheng Wu has made 2 new python packages available for SAM in the last day or so - segment-anything-py and segment geospatial: https://github.com/opengeos/segment-geospatial
caer
- Show HN: Caer – A lightweight GPU-accelerated Vision library in Python
- I wrote a lightweight GPU-accelerated Vision library in Python
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Jetson nano python3 illegal instruction problem
I think it may have. If you look at line 10 of https://github.com/jasmcaus/caer/blob/master/configs.ini, you’ll see that caer has numpy and opencv-contrib-python dependencies that get referenced in its setup.py. If I recall correctly, pip on the nano doesn’t pick up the default numpy and opencv-python system installs, so when you go to install something like caer that has them as dependencies, it will install new copies except the wheel files that it grabs are incompatible. The solution I have found to work is to run something similar to the command above: “pip3 install —no-binary caer —no-binary numpy—no-binary opencv-contrib-python —no-binary typing-extensions —no-binary mypy —force-reinstall caer”. Some of those —no-binary options may not be necessary but they’ll at least ensure pip grabs the source for each of the dependencies and rebuilds it locally rather than using an imcompatible version. This command will take awhile! But you only should have to do it once.
- jasmcaus/caer Modern Computer Vision on the Fly
- Caer: High-performance Vision Library in Python (faster than Torchvision)
- Caer – A GPU-accelerated Computer Vision library (faster than Torchvision)
- jasmcaus/caer lightweight, scalable Computer Vision library for high-performance AI research
- Caer – A GPU-Accelerated Computer Vision Library in Python
What are some alternatives?
autodistill - Images to inference with no labeling (use foundation models to train supervised models).
fiftyone - The open-source tool for building high-quality datasets and computer vision models
sam-hq - Segment Anything in High Quality [NeurIPS 2023]
img2table - img2table is a table identification and extraction Python Library for PDF and images, based on OpenCV image processing
SegmentAnythingin3D - Segment Anything in 3D with NeRFs (NeurIPS 2023)
opencv - Haskell binding to OpenCV-3.x
Single-Image-Dehazing-Python - python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
moviepy - Video editing with Python
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
tsdf-fusion-python - Python code to fuse multiple RGB-D images into a TSDF voxel volume.