use tensorflow 2.0 to tell a dog and cat from a specified picture

Last update: Oct 22, 2021

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dog_or_cat

use tensorflow 2.0 to tell a dog and cat from a specified picture

This is one of the classic experiments for the introduction of deep learning: the cat and dog classification experiment. We will use tensorflow 2.0 to classify the cats and dogs in the pictures. Due to the limitation of file upload size, I deleted most of the pictures. If you want to obtain the original data set, you can go to https://momodel.cn/explore/60e6b9babb5ba072ad72755d?type=dataset to download it.

In addition, the local model that I use CPU for training has been deleted by me, leaving only one model of others on the network for reference. If you want to improve the experimental accuracy, you can try to increase epochs or improve the model. It is recommended to use GPU to accelerate the model training.

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GitHub Repository

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use tensorflow 2.0 to tell a dog and cat from a specified picture

Related tags

Overview

dog_or_cat

Owner

你这个代码我看不懂

It's final year project of Diploma Engineering. This project is based on Computer Vision.

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

Code for "Retrieving Black-box Optimal Images from External Databases" (WSDM 2022)

Speech Recognition using DeepSpeech2.

李云龙二次元风格化!打滚卖萌，使用了animeGANv2进行了视频的风格迁移

Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

Curved Projection Reformation

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ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

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Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

Python script that allows you to automatically setup your Growtopia server.

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Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

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