labelme2coco
HugsVision
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labelme2coco | HugsVision | |
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1 | 1 | |
246 | 188 | |
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3.8 | 0.0 | |
7 days ago | 9 months ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 only | MIT License |
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labelme2coco
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What's A Simple Custom Segmentation Pipeline?
I would also suggest labelme, it's pretty easy to use. Just type "labelme" in the shell after pip installing and you will see the GUI. There are tools to convert to coco format (like https://github.com/fcakyon/labelme2coco) if needed, for instance for Detectron2.
HugsVision
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[R] HugsVision: A easy-to-use HuggingFace wrapper for computer vision
Find more tutorials and informations about HugsVision on GitHub
What are some alternatives?
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
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
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)
fashionpedia-api - Python API for Fashionpedia Dataset
coco-viewer - Minimalistic COCO Dataset Viewer in Tkinter
ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
CoordConv
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.