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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你这个代码我看不懂

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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

Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 short.

Speech recognition tool to convert audio to text transcripts, for Linux and Raspberry Pi.

Python implementation of "Elliptic Fourier Features of a Closed Contour"

Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation

We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

Implementation of ConvMixer-Patches Are All You Need? in TensorFlow and Keras

This is the official github repository of the Met dataset

ACL'2021: LM-BFF: Better Few-shot Fine-tuning of Language Models

Official Pytorch implementation of "DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network" (CVPR'21)

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