cnn-in-welding
fashion-mnist
| cnn-in-welding | fashion-mnist | |
|---|---|---|
| 1 | 15 | |
| 42 | 11,439 | |
| - | 0.0% | |
| 1.8 | 0.0 | |
| over 3 years ago | almost 2 years ago | |
| Python | Python | |
| - | MIT License |
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cnn-in-welding
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Detecting Welding Defects in Steel Plates using Computer Vision Algorithms
The dataset contains two directories. The raw images are stored in images directory and the segmented images are stored in labels directory.
fashion-mnist
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Logistic Regression for Image Classification Using OpenCV
In this case there's no advantage to using logistic regression on an image other than the novelty. Logistic regression is excellent for feature explainability, but you can't explain anything from an image.
Traditional classification algorithms but not deep learning such as SVMs and Random Forest perform a lot better on MNIST, up to 97% accuracy compared to the 88% from logistic regression in this post. Check the Original MNIST benchmarks here: http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/#
- Pre-Trained ML models for labeling retail images? Upload an image of a dress shirt and the labels output are “long sleeve, men’s, button down, collar, formal, dress shirt” or better?
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The Paradigm Shift Towards Multimodal AI Jina AI MLOps for Multimodal AI Neural Search and Creative AI
Compared to NLP, I came to the field of computer vision (CV) pretty late. While at Zalando in 2017, I published a paper on the Fashion-MNIST dataset. This dataset is a drop-in replacement of Yann LeCun's original MNIST dataset from 1990 (a set of simple handwritten digits for benchmarking computer vision algorithms.) The original MNIST dataset was too trivial for many algorithms – shallow learning algorithms such as logistic regression, decision trees, and support vector machines could easily hit 90% accuracy, leaving little room for deep learning algorithms to shine.
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MNIST classification using pytorch/I will do data science, data analysis, machine learning in python
Fashion MNIST: This dataset from Zalando Research contains images of 10 classes consisting of clothing apparel and accessories like ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, etc. instead of handwritten digits. The images are grayscale just like the original MNIST.
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Computer Vision 101 - Fashion MNIST
Link to the dataset : Fashion Mnist You dont need to download the dataset manually, they are included as part of pytorch Its better if you use jupyter-notebook as the code in this blog is a step by step process with data visualisation in between for better understanding.
- How to produce data visualizations like this?
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Image recognition and linear regression.
[1] https://github.com/zalandoresearch/fashion-mnist
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A New Google AI Research Study Discovers Anomalous Data Using Self Supervised Learning
New Google AI research introduces a 2-stage framework that uses recent progress on self-supervised representation learning and classic one-class algorithms. This framework is simple to train and shows SOTA performance on various benchmarks, including CIFAR, f-MNIST, Cat vs. Dog, and CelebA. Following that, they offer a novel representation learning approach for a practical industrial defect detection problem using the same architecture. On the MVTec benchmark, the framework achieves a new state-of-the-art.
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Staying in Tune: A guide to optimizing hyperparameters
Xiao H, et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747
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Machine Learning Orchestration on Kubernetes using Kubeflow
About the Dataset Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the exact image size and structure of training and testing splits. source: https://github.com/zalandoresearch/fashion-mnist
What are some alternatives?
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
kmnist - Repository for Kuzushiji-MNIST, Kuzushiji-49, and Kuzushiji-Kanji
autogluon - Fast and Accurate ML in 3 Lines of Code
kubeflow - Machine Learning Toolkit for Kubernetes
zozo-shift15m - SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
tape - Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Anime-face-generation-DCGAN-webapp - A port of my Anime face generation using Pytorch into a Webapp
fashion-mnist-kfp-lab - A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.
XVFI - [ICCV 2021, Oral 3%] Official repository of XVFI