Brain-Tumor-Segmentation-And-Classification
caer
| Brain-Tumor-Segmentation-And-Classification | caer | |
|---|---|---|
| 3 | 8 | |
| 18 | 749 | |
| - | - | |
| 3.0 | 0.0 | |
| 9 months ago | 7 months ago | |
| Python | Python | |
| MIT License | MIT License |
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Brain-Tumor-Segmentation-And-Classification
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Brain Tumor Segmentation and Classification using ResUnet.
Source code: Github
- Github project showcase: Brain Tumor Segmentation and Classification using ResUnet.
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Brain Tumor Segmentation and Classification using deep learning.
GitHub repo : Brain Tumor Segmentation and Classification
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?
dipy - DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
fiftyone - The open-source tool for building high-quality datasets and computer vision models
MONAILabel - MONAI Label is an intelligent open source image labeling and learning tool.
img2table - img2table is a table identification and extraction Python Library for PDF and images, based on OpenCV image processing
medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
opencv - Haskell binding to OpenCV-3.x
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
Single-Image-Dehazing-Python - python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"
FedCV - FedCV: An Industrial-grade Federated Learning Framework for Diverse Computer Vision Tasks
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!