benchmark VS fashion-mnist

Regarding Milvus. Well, there are a few essential differences between our projects: - Unlike Milvus, we perform filtering during the search in the vector index, which keeps retrieval complexity close to logarithmic - same as in original HNSW. - We can support complex types of filterable payloads like geo-points - it is not a trivial problem to keep the HNSW search graph connected during filtering. We solved it in our custom implementation of the HNSW index. - Unlike Milvus, we perform a query-planning phase to determine an optimal strategy of executing queries with filters - Qdrant uses Rust programming language - it gives us an advantage in avoiding stop-the-world issues of languages with garbage collection. We also have a retrieval speed benchmark - https://github.com/qdrant/benchmark.

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