nmccann1991/Intel-Natural-World-Study

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A Natural Look into our AI-Driven World: An Image Classification Lookthrough
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<img src="https://images.pexels.com/photos/869258/pexels-photo-869258.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1">

TL;DR
This image classification project identifies settings and categories of images automatically from Intel's natural world dataset.
Our method utilizes Tensorflow on Python 3 to execute a classification process with a model accuracy of 74.8%. Not bad, but this suggests that there are opportunities for improvement.
The loss in the test sets that is affecting accuracy comes down to Glacier and Sea images.

AI is changing the world as we know it. Companies look to train models based on recurring patterns, and visuals such as images make for great training material to help them identify and improve upon their ability to predict patterns and accuracy.
For this project, we will be using Tensorflow. Tensorflow is an ML package from Google that enables analysts and engineers to develop everything from Simple Neural Networks to Image Classification.
We're gonna focus on the latter, so this project will concentrate on using Keras.

The dataset used: https://www.kaggle.com/datasets/puneet6060/intel-image-classification

## Installation
I highly recommend using [VS Code](https://code.visualstudio.com/) to manage your python environment.
From there, install Tensorflow by first installing Miniconda and then Tensorflow. Full instructions can be found [here](https://www.tensorflow.org/install/pip#macos_1)

I used the following for my Macbook Pro M1 Max laptop (Apple Silicon) machine:
```
#Miniconda
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh
bash Miniconda3-latest-MacOSX-x86_64.sh

#Conda activation
conda create --name tf python=3.9
conda conda activate tf

#Pip Upgrade
pip install --upgrade pip

#Pip Install
pip install tensorflow

#Verify
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
```
## Access to Project
You can access the Jpegs by visiting https://drive.google.com/file/d/1Ox3W_AJdBjg5Sc6tOn9KikR2BB8zndxh/view?usp=drive_link.
To run the ipynb, download and unzip the image file and then change the file directory in the notebook to the location of the unzipped images.

原始信息汇总

数据集概述

数据集名称

• 名称: Intel Image Classification
• 来源: Kaggle

数据集用途

• 用途: 用于图像分类项目，旨在自动识别图像中的场景和类别。

数据集特点

• 技术实现: 使用Tensorflow和Python 3进行图像分类处理。
• 模型性能: 模型准确率为74.8%。
• 挑战: 在测试集中，Glacier和Sea类别的图像影响了准确率。

数据集使用工具

• 主要工具: Tensorflow, Keras

数据集访问

• 图像访问: 可通过Google Drive下载Jpegs。
• 运行环境: 推荐使用VS Code管理Python环境，并通过Miniconda安装Tensorflow。