6229 Open source projects that are alternatives of or similar to Grocery Product Classification

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018

A machine learning experiment

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

CNN image classifier implemented in Keras Notebook 🖼️.

Deep learning codes and projects using Python

Trial on kaggle imagenet object localization by yolo v3 in google cloud

Image Tampering Detection using ELA and CNN

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)

Implementation of the model used in the paper Protest Activity Detection and Perceived Violence Estimation from Social Media Images (ACM Multimedia 2017)

猫狗大战

Programming Assignments and Lectures for Stanford's CS 231: Convolutional Neural Networks for Visual Recognition

Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.

Deep Learning Food Classifier for iOS using Keras and Tensorflow

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

Ipython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets.

Best Practices, code samples, and documentation for Computer Vision.

Object classification with CIFAR-10 using transfer learning

Deep Learning: Image classification, feature visualization and transfer learning with Keras

Code for our paper "Generalized Orderless Pooling Performs Implicit Salient Matching" published at ICCV 2017.

Official Repsoitory for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]

🙄 Difficult algorithm, Simple code.

IPyPlot is a small python package offering fast and efficient plotting of images inside Python Notebooks. It's using IPython with HTML for faster, richer and more interactive way of displaying big numbers of images.

Efficient Learning of Augmentation Policy Schedules

Food Classification with Deep Learning in Keras / Tensorflow

A little benchmark comparing Pandas data frames serialization formats

Materials for a Python Beginner's Course. First given at the Royal Society of Biology. Designed and delivered by Chas Nelson and Mikolaj Kundegorski.

3-day dive into deep learning at csc

CCKS 2020: 面向中文短文本的实体链指任务

Simple and flexible progress bar for Jupyter Notebook and console

Exploratory Project on Fast Screen Space Global Illumination

Series of Tensorflow Tutorials

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

《推荐系统实践》代码与读书笔记，在线阅读地址：https://relph1119.github.io/recommendation-system-practice-notes

A free tutorial for Apache Spark.

Data Science

Python code for YouTube videos.

COSE474: Deep Learning @ Korea University

Materials for the 2016 Astro Hack Week

Knowledge distillation implemented in TensorFlow

Pyncov-19: Learn and predict the spread of COVID-19

Detecting Covid-19 from X-ray

Bu repoda veri artırma (data augmentation) ile ilgili pratik uygulamalara ulaşabilirsiniz.

Code for Coursera's Machine Learning and Data Analysis specialization

A collection of resources and Jupyter notebooks from my blog.

Notes for getting to know the Julia programming language in one day.

Literature and Data - Spring 2016 Data Science Connector Course

This workshop has been deprecated check out the new workshop here https://github.com/aribornstein/cvworkshop

Machine learning algorithms implemented by pure numpy

MachineLearningExp.com projects

Python package for computing the Curie depth from the magnetic anomaly

Predicting the probability that a diagnosed breast cancer case is malignant or benign based on Wisconsin dataset

Constraint Satisfaction Problem lab to solve the N-Queens problem