Brain-Tumor-Segmentation-And-Classification
albumentations
| Brain-Tumor-Segmentation-And-Classification | albumentations | |
|---|---|---|
| 3 | 28 | |
| 18 | 13,425 | |
| - | 0.9% | |
| 3.0 | 8.9 | |
| 9 months ago | 5 days 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
albumentations
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Augment specific classes?
You can use albumentations if you are comfortable with using open source libraries https://github.com/albumentations-team/albumentations
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Ask HN: What side projects landed you a job?
One of the members of the core team of our open-source library https://albumentations.ai/
It was not the only reason he was hired; it was a solid addition to his already good performance at the interviews.
Or at least that is what the hiring manager later said.
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The Lack of Compensation in Open Source Software Is Unsustainable
I am one of the creators and maintainers of https://albumentations.ai/.
- 12800+ stars
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Burn Deep Learning Framework Release 0.7.0: Revamped (de)serialization, optimizer & module overhaul, initial ONNX support and tons of new features.
Is something planned to support data augmentations? Something like https://albumentations.ai/
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How to label augmented images for training YOLO algorithm?
Here you go: https://albumentations.ai/
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Unstable Diffusion bounces back with $19,000 raised in one day, by using Stripe
I think they should use some data augmentation techniques like I am using for Infinity AI if you wanna see more here. Note that most of these do not work for image generation.
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Tokyo Drift : detecting drift in images with NannyML and Whylogs
Our second approach was a more automated one. Here the idea was to try out an image augmentation library, Albumentations, and use it for adversarial attacks. This time, instead of one-shot images, we applied the transformations at random time ranges. We chose for these transformations also to be more subtle than then one-shot images, such as vertical flips, grayscaling, downscaling, …
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[D] Improve machine learning with same number of images
Check out albumentations. If your use case is segmentation, check out the offline augmentation of this project
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What are the best programs/scripts for image augmentation of YOLO5 training dataset. Something like roboflow but free)
I think this is the most popular open source project: https://github.com/albumentations-team/albumentations
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To get dataset for face image restoration.
You can also curate your own dataset by using open source images (https://universe.roboflow.com/search?q=faces%20images%3E1000) and open source augmentations (https://github.com/albumentations-team/albumentations). Or you can do use the augmentation UI (https://docs.roboflow.com/image-transformations/image-augmentation) to apply noise, blurring, shear, crop, etc.
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.
imgaug - Image augmentation for machine learning experiments.
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
MONAILabel - MONAI Label is an intelligent open source image labeling and learning tool.
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
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.
autoalbument - AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
FedCV - FedCV: An Industrial-grade Federated Learning Framework for Diverse Computer Vision Tasks
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
ttach - Image Test Time Augmentation with PyTorch!