asv VS fashion-mnist

> All these workflows are a derivation of the source in the repository and keeping them close together has a great aesthetic.

I agree. Version control is a great enabler, so using it to track "sources" other than just code can be useful. A couple of tools I like to use:

- Artemis, for tracking issues http://www.chriswarbo.net/blog/2017-06-14-artemis.html

- ASV, for tracking benchmark results https://github.com/airspeed-velocity/asv (I use this for non-Python projects via my asv-nix plugin http://www.chriswarbo.net/projects/nixos/asv_benchmarking.ht... )

scikit-image, the project that commissioned this task, uses Airspeed Velocity, or asv, for their benchmark tests.

Problem - The project currently uses Airspeed Velocity for tracking the memory changes. But I am having a lot of trouble setting this up and using this tool for monitoring memory consumption on a regular basis. Are you guys aware of some other open-source tools that I can use instead of this? I am stuck with this thing for some time now. I would appreciate any help.

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/#

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.

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.

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.

[1] https://github.com/zalandoresearch/fashion-mnist

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.

Xiao H, et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747

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