Click on the above“Beginner Learning Vision” to selectStar or “Pin” Heavyweight content delivered first-hand Originally published by | Machine Learning AI Algorithm Engineer The link to the VOC dataset used is provided in the article, the pre-trained model has been uploaded to GitHub, and I used Colab Pro for the environment, everyone can download the … Read more
Click the above “Beginner’s Guide to Vision” and choose to add a Star or “Top” Essential content delivered promptly Introduction This article summarizes important points to consider when implementing checkpoint resume training with PyTorch, along with detailed code explanations. Recently, while trying to train a classification problem using CIFAR10, I found that the dataset is … Read more
Click on the above “Beginner’s Guide to Vision” to choose to add “Star” or “Pin“ Important content delivered promptly Author: Alxander@Zhihu (authorized) Source: https://zhuanlan.zhihu.com/p/448284000 Editor: Jishi Platform Jishi Guide During the training process in deep learning, due to random initialization and the randomness of sample reading, repeated experimental results may differ, with some variations being … Read more
Click on "Xiaobai Learns Vision" above, select to add "Star" or "Top" Heavyweight content delivered first License Plate Recognition Overview License plate recognition based on deep learning, where the vehicle detection network directly uses YOLO for detection. Then, a network is used to detect and recognize the license plate number. The license plate detection network … Read more
Click on "Xiaobai Studies Vision" above, select to add "Star" or "Top" Heavy content, delivered first time For academic sharing only, does not represent the position of this public account. Contact for deletion if infringing. Reprinted from:Source | Jishi Platform Author | Jack Stark@Zhihu The best resource for PyTorch is the official documentation. This article … Read more
This article was compiled by a PhD student from Harbin Institute of Technology over the course of six months, consisting of a total of fourteen chapters. It starts with the basics of creating tensors and gradually deepens, covering almost all commonly used functions! [From Beginner to Pro in PyTorch, AI Deep Learning Neural Network Learning … Read more
“Standing on the shoulders of giants” — this is precisely the benefit that pre-trained models bring in deep learning. Training deep neural networks from scratch is both resource-intensive and time-consuming. However, the good news is that PyTorch’s pre-trained models can effectively solve this issue. PyTorch provides models that have learned robust features on large datasets, … Read more
Every algorithm has its applicable range, and understanding its pros and cons can help avoid errors caused by inappropriate use. This article summarizes the pros and cons of common machine learning algorithms for reference. The sources are from “Machine Learning: Using R, Tidyverse, and mlr” (Algorithms 1 to 17) and “Neural Networks: Implementation in R” … Read more
Introduction Reference code homepage, still the great Julia Silge’s code, who is also a main author of tidymodels. Overall process The original official tutorial URL: https://juliasilge.com/blog/xgboost-tune-volleyball/ Notes 1. Due to poor external data currently, the data used is the test data from the tidytuesdayR package. 2. Tidymodels is an integrated R language machine learning environment … Read more
A Powerful Tool in Kaggle: The XGBoost Algorithm Hi, everyone, I am Chenxi. This article is also based on a request from a friend who has been pushed by their boss to build a model, so they reached out to me for some guidance. Student A: Chenxi, Ionly know R, is there a model that … Read more