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The getimg.ai image generation and editing API is organized around REST. Our API has predictable resource-oriented URLs engineered around different AI model families and creation pipelines. It accepts JSON-encoded request bodies, returns JSON-encoded responses, and uses standard HTTP response codes, authentication, and verbs.

This is the documentation for version 1.0 of the API. Last update on May 29, 2023.

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The best AI image generators to create art from text | Digital Trends

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The best AI image generators to create art from text | Digital Trends

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Interested in trying out AI-generated art? Here are some of the best text-to-art generators that might match your artistic style.

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10 Best AI Art Generators (2022) - Unite.AI

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Artificial intelligence (AI) is not only affecting industries like business and healthcare. It is also playing an increasing role in the creative industries by ushering in a new era of AI-generated art. AI technologies and tools are often widely accessible to anyone, which is helping to create an entirely new generation of artists.

We often hear that AI is going to automate away or take over all human tasks, including those in art, film, and other creative industries. But this is far from the case. AI is a supplemental tool that artists can use to explore new creative territory.

These creative AI tools can be used by anyone to create art, which can often be turned into NFTs.

Let's take a look at the 10 best AI art generators:

We might be a little biased since this is the first of many AI tools that will be launched by the unite.ai team. It uses the latest in open source stable diffusion technology to offer AI art generation.

Unlike competing websites that offer stable diffusion solutions - the website is FREE. We will reiteratively and continuously improve our algorithm as our team expands and continue to add resources, and optimize the machine learning algorithms that are layered on top of this software.

The process is simple - Visit the website and choose from 5 different image dimensions, enter your search term, and like magic the website will auto-generate images.

Some of the core features:

Simple and easy to use - Takes less than 10 Seconds to start

Create memes from your images

Use existing pre-trained recipes

Select from various size options

Advanced setting for power users

GetIMG offers a suite of AI tools that sets it apart from most image generators. Generate original images at scale, modify photos, expand pictures beyond their original borders, or create custom AI models.

They offer over 20 AI models, from Stable Diffusion to custom community styles.

With the advanced editor, you can generate missing parts of any photo or create stunning large art pieces on infinitely sized canvas. There literally are no limits. Easily modify small details or change whole visual features on any picture. Use AI inpainting to remove unwanted objects from images, or alter any other elements. Just erase part of the image and tell AI what to render in empty space.

Get a personalized AI model. It's as easy as uploading 10 pictures. Whether you want to create AI avatars for yourself or your team, need to render beautiful images of your product in different scenarios, or just want to have your own AI model to generate ideas with your style. They've got you covered. Every model is hosted available to use in seconds.

Super fast - Up to 10 images in seconds

Over 20 AI models

Create Custom AI Avatars

Personalize your own AI to render images in your style

Easily modify images with text prompts

Super customizable

ArtSmart AI is easier to use than most other platforms including Midjourney and allows more control over outputs. The platform offers a number of tools which helps users assemble better and more nuanced prompts. This includes prompting for Camera Set-Up, Camera Angles, Time & Weather, and Painting Styles.

Using the Tune feature, you can train ArtSmart AI's model on any photo you like and get it to generate variations for you.

One notable feature is the Explore and Community section which offers a searchable library and allows you to be inspired by different user's creations.

Various tools that are offered include:

Background Remover

Inpainting & Outpainting

Image to Image & Image to Prompt

Face Enhancer

Image Upscaler

LimeWire, previously a popular file-sharing platform, has re-launched as an AI-focused content publishing & community platform.

Currently live is the LimeWire AI Studio, it uses a variety of open source image generation models including SDXL, SD 2.1, DALL-E2, and they are working on a proprietary model.

LimeWire also has a crypto utility token called LMWR, which can be used to pay for prompts, earn ad revenue share for AI content, and more. LMWR can be bought and traded on many large exchanges including Kraken.

LimeWire will eventually launch generative AI music & video. The eventual goal is to build a multi-content AI Studio that allows users to create full music tracks & sophisticated video content using AI.

LimeWire allows users to automatically mint AI-generated content as NFTs on the Polygon and Algorand blockchain.

Click Here to visit LimeWire.

NightCafe is one of the big names in the world of AI art generators. It's known for having more algorithms and options than other generators, but it's also extremely easy for novice users to get the hang of. NightCafe is based on a credit system, but it has a generous free tier, and plenty of options to "earn" credits by participating in the community. You can also buy credits and use the code UNITEAI for a 15% discount.

Here are some of the main features of NightCafe:

You own your creations

More algorithms than other generators

Lots of control for advanced users

Earn credits by participating in the community

Lots of social features, and a vibrant, helpful community

Organise your creations into collections

Bulk-download all your images

Create videos

Buy a print of your artwork

While the underlying algorithms are always changing across all the AI art generators listed here, NightCafe's list of other features is what sets it apart.
Use code UNITEAI for 15% off your first payment.
Read our NightCafe Review or visit NightCafe.

Shutterstock is an established company that trades on the New York Stock Exchange (NYSE). They provide stock photography, stock footage, stock music, editing tools and as of 2023 they provide the best tools to generate your own images and art. This was achieved by integrating OpenAI's DALL-E 2's image-generating AI system.

DALL-E was a gamechanger, and version 2 is even better. DALL-E 2 is a successor designed to generate more realistic images at higher resolutions that can combine concepts, attributes, and styles.

Furthermore, what sets the Shutterstock solution apart is combining this state of the art AI generation system with the ease of use of the Shutterstock platform. They have mastered the UX (user interface) and they make it fast and simple to get started.

There's 3 simple steps:

1. Imagine anything! Then type it into the search bar. Click Generate.

2. Shutterstock generates a few versions for you to choose from.

3. Edit the image in Create, if you like. Or just license and download.

You can license up to 10 AI-generated images for free with Shutterstock's Free Trial Offer.
Click Here to visit Shutterstock.

Midjourney needs no introduction, it offers amazing art generation capabilities, the only problem is you need to use the Discord instant messenger App which for many users is a deal breaker. Users can interact with the MidJourney bot by sending it a direct message or by inviting it to another server on their official Discord server.

Midjourney is a generative AI platform and service developed and maintained by the San Francisco-based independent research laboratory, Midjourney, Inc. It produces images based on textual descriptions, known as "prompts," akin to technologies like OpenAI's DALL-E and Stability AI's Stable Diffusion.

The platform is best known for creating photorealistic images but does require mastering prompting.

DALL-E 2 is an AI image generator developed by OpenAI. In just a few minutes, you can create highly realistic images with the AI.

DALL-E 2 is showing incredible potential. According to OpenAI, the tool can be used to create illustrations, design products, and generate new ideas for business.

DALL-E 2's easy-to-use interface makes it possible for anyone to create high-quality images with AI. This means that not only professional artists will find value in DALL-E 2, but amateur artists can also use the tool.

One of the best tools offered by DALL-E 2 is its paintbrush, which enables you to add details like shadows, highlights, and more to your image. Tools like the paintbrush allow you to create complex images with multiple layers, each one customized with its own properties.

Here are some of the main features of DALL-E 2:

Another one of the best AI art generators is Deep Dream Generator by Aifnet. One of the most popular AI art generators on the market, Deep Dream is an online tool that enables you to create realistic images with AI.

Deep Dream relies on a neural network that was trained with millions of images. It is easy-to-use, only requiring you to upload an image before the tool generates a new image based on the original.

One of the main applications of Deep Dream is to use it to create artwork, since it uses different painting styles to generate images that appear to be from different places or periods of time.

The tool allows you to choose a category such as animals or landscapes before it creates a realistic image based on it. On top of all of this, Deep Dream allows you to choose three styles: Deep Style, Thin Style, or Deep Dream. After choosing a style, you can preview the image.

They have also released their own text to Image software that they have named Text 2 Dream.

Here are some of the main features of Deep Dream:

Creates realistic images with AI

Neural network trained on millions of images

Different painting styles

Categories of images

Three separate styles

Text to image

One of the most well-known names among AI art generators is Artbreeder, which is an image quality enhancer. The tool enables you to produce different variations of an image through the use of machine learning.

You can create landscapes, anime figures, portraits, and various other works of art on a single platform. Some of the other tools include the ability to change facial features like skin color, hair, and eyes. You can also turn photos into animated figures.

Another top feature of Artbreeder is that it offers thousands of illustrations and allows you to manage them in folders. The results can then be downloaded in JPG or PNG format.

Here are some of the main features of Artbreeder:

Notable Mentions:

This open source AI image generator had over 3000 people using it the first month, it creates realistic images from scratch. Like some of the other AI art generators, it is easy to use by anyone. It only takes a few steps to create one of the realistic images.

Stablecog uses Stable Diffusion, was made with SvelteKit, and uses Supabase for recording of metadata and for managing the default cog cluster.

Here are some of the main features of Big Sleep:

Founded in 2016, DeepAI was created with the goal of democratizing AI through open-source software. It offers various tools that can be used to create realistic images.

DeepAI enables you to create as many images as you'd like, and each one is unique. It is highly customizable, allowing you to change the amount of detail, colors, textures, and more. If you type an illustration, DeepAI can immediately generate a resolution-independent vector image.

Some of the other tools offered by DeepAI include StyleGAN and BigGAN, which can also create realistic images. The CartoonGAN tool enables you to turn the images into cartoons.

Here are some of the main features of DeepAI:

StarryAI is an AI art generator that allows you to turn the art into NFTs. It doesn't require any input from the user. It can process images with a machine learning algorithm.

One of the best parts about StarryAI is that it gives you full ownership of the created images to be used personally or commercially. It acts as a free NFT generator, which is its main selling point. The technology is constantly improving, but there have already been incredible examples of art created with the app.

Here are some of the main features of StarryAI:

CF Spark, an AI image generator offered by Creative Fabrica, a digital marketplace for print-on-demand assets, fonts, and graphics, has generated millions of prompts. One unique feature of this platform is that users can monetize their generative AI files by offering them for sale to other members, making it the first of its kind to offer such an opportunity to creators.

To use CF Spark, creators input a prompt that generates four images they can choose to publish on their page. Other users on the platform can re-prompt the AI images to receive different results and upload images of their creations with AI-generated art, such as a T-shirt design.

Typically, modern AI art generators take several minutes to create images based on user prompts. Although great art takes time, a CF Spark subscription can help speed up the creation process. Subscribers receive 1000 speed credits that enable them to bypass the queue and create AI art much more quickly.

Here are some of the main features of CF Spark:

Monetize art creations

Re-prompt other AI images

Speed credits for faster image generation

Easy-to-use

Fotor is another one of the best AI tools for creating NFT art. It requires minimal effort, with the generator offering advanced AI tools to create art in minutes. It is also one of the most user-friendly tools for creating NFT art.

All you have to do is upload an image and select the style of art you want to apply to it. You can also create layers or add personal touches quickly and easily. The tool doesn't require you to create an account to use the software or download the artworks.

Here are some of the main features of Fotor:

Runway ML allows you to generate images by training and using machine learning models. With the tool, you create models that generate realistic image styles in a variety of ways. On top of that, you can use Runway ML to create animations and 3D models.

If you want to create video projects, Runway ML includes a video editor tool that allows you to replace the background images of videos.

Some of the tools utilized by Runway ML include relative motion analysis, which allows it to analyze what you're trying to do. It also has out-of-the-box object recognition, which helps you easily identify objects in images or videos.

Here are some of the main features of Runway ML:

Closing out our list of 10 best AI art generators is WOMBO Dream, which is an AI NFT creator app developed by Canadian startup WOMBO. It is considered by many to be the best all-around NFT creator app.

Dream can transform existing photos into cartoons or faux paintings, as well as use a complex algorithm to turn words and phrases into unique works of art. The tool allows you to choose from a variety of art styles, or you can opt for futuristic landscapes.

Here are some of the main features of WOMBO Dream:

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10 Best AI Art Generators (November 2023)

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Artificial intelligence (AI) is not only affecting industries like business and healthcare. It is also playing an increasing role in the creative industries by ushering in a new era of AI-generated art. AI technologies and tools are often widely accessible to anyone, which is helping to create an entirely new generation of artists.

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In this article, we will briefly introduce the field of artificial intelligence, particularly in computer vision, the challenges involved, the existing modern solutions to these challenges and how you can apply these solutions conveniently and easily without taking much time and effort.

Artificial Intelligence has for decades been a field of research in which both scientists and engineers have been making intense efforts to unravel the mystery of getting machines and computers to perceive and understand our world well enough to act properly and serve humanity. One of the most important aspect of this research work is getting computers to understand visual information (images and videos) generated everyday around us. This field of getting computers to perceive and understand visual information is known as computer vision.

During the rise of artificial intelligence research in the 1950s to the 1980s, computers were manually given instructions on how to recognize images, objects in images and what features to look out for. This method are traditional algorithms and were called Expert Systems, as they require that humans take the pain of identifying features for each unique scene of object that has to be recognize and representing these features in mathematical models that the computer can understand. That involves a whole lot of tedious work because there are hundreds and thousands of various ways an object can be represented and there are thousands (or even millions) of different scenes and objects that uniquely exist, and therefore finding the optimized and accurate mathematical models to represent all the possible features of each objects or scene, and for all possible objects or scene is more of work that will last forever.

Then, in the 1990s, the concept of Machine Learning was introduced and it ushered in an era in which instead of telling computers what to look out for in recognizing scenes and objects in images and videos, we can instead design algorithms that will make computers to learn how to recognize scenes and objects in images by itself, just like a child learns to understand his/her environment by exploring. Machine learning opened the way for computers to learn to recognize almost any scene or object we want them too.

With the emergence of powerful computers such as the NVIDIA GPUs and state-of-the-art Deep Learning algorithms for image recognition such as AlexNet in 2012 by Alex Krizhevsky et al, ResNet in 2015 by Kaeming He et al, SqueezeNet in 2016 by Forrest Landola et al, DenseNet in 2016 by Gao Huang et al, to mention a few, it is possible to put together a number of pictures (more like image books for computers) and define an artificial intelligence model to learn features of scenes and objects in these pictures by itself and use the knowledge gained from the learning process to recognize all other instance of the type of scene or objects it will encounter after.

To train an artificial intelligence model that can recognize whatever you want it to recognize in pictures, it traditional involves lots of expertise in Applied Mathematics and use of Deep Learning libraries, not to mention the amount of time involved and stress you have to go through to write the code for the algorithm and fit the code to your images. This is where we have provided our solutions.

Our team at AI Commonshas developed a python library that can let you train an artificial intelligence model that can recognize any object you want it to recognize in images using just 5 simple lines of python code. The python library is ImageAI, a library built to let students, developers and researchers with all levels of expertise to build systems and applications with state-of-the-artcomputer vision capabilities using between 5 to 15 simple lines of code. Now, let us walk you through creating your first artificial intelligence model that can recognize whatever you want it to.

To train your artificial intelligence model, you need a collection of images called a dataset. A dataset contains hundreds to thousands of sample images of objects you want your artificial intelligence model to recognize. But you don't have worry! We are not asking you to go and download thousands of pictures right now just to train your artificial intelligence model. For this tutorial, we have provided a dataset called IdenProf. IdenProf (Identifiable Professionals) is a dataset that contains 11,000 pictures of 10 different professionals that humans can see and recognize their jobs by their mode of dressing. The classes of professionals whose pictures are in this dataset are as below:

· Chef

· Doctor

· Engineer

· Farmer

· Firefighter

· Judge

· Mechanic

· Pilot

· Police

· Waiter

This dataset is split into 9000 (900 pictures for each profession)pictures to train the artificial intelligence model and 2000 (200 pictures for each profession)pictures to test the performance of the artificial intelligence model as it is training. IdenProf has been properly arranged and made ready for training your artificial intelligence model to recognize professionals by their mode of dressing. For reference purposes, if you are using your own image dataset, you must collect at least 500 pictures for each object or scene you want your artificial intelligence model to recognize. To train any image dataset you collect yourself with ImageAI, you must arrange the images in folders as seen in the example below:

idenprof//train//chef// 900 images of chefs idenprof//train//doctor// 900 images of doctors idenprof//train//engineer// 900 images of engineer idenprof//train//farmer// 900 images of farmers idenprof//train//firefighter// 900 images of firefighters idenprof//train//judge// 900 images of judges idenprof//train//mechanic// 900 images of mechanics idenprof//train//pilot// 900 images of pilots idenprof//train//chef// 900 images of chef idenprof//train//police// 900 images of police idenprof//train//waiter// 900 images of waiters idenprof//test//chef// 200 images of chefs idenprof//test//doctor// 200 images of doctors idenprof//test//engineer// 200 images of engineer idenprof//test//farmer// 200 images of farmers idenprof//test//firefighter// 200 images of firefighters idenprof//test//judge// 200 images of judges idenprof//test//mechanic// 200 images of mechanics idenprof//test//pilot// 200 images of pilots idenprof//test//chef// 200 images of chef idenprof//test//police// 200 images of police idenprof//test//waiter// 200 images of waiters

Now that you have understand how to prepare own image dataset for training artificial intelligence models, we will now proceed with guiding you training an artificial intelligence model to recognize professionals using ImageAI.

· First you must download the zip of IdenProf dataset via this link. Also you can view all the details and sample results of artificial intelligence models trained to recognize professions in the IdenProf GitHub repository whose link is below.
https://github.com/OlafenwaMoses/IdenProf
· Because training artificial intelligence models require high performance computer systems, I strongly advice that you ensure your computer/laptop that you want to use for this training has NVIDIA GPU. Alternatively, you can use Google Colab for this experiment has it offers a free NVIDIA K80 GPU for experiments.

· Then you have to install ImageAI and its dependencies.

1) Download and install Python 3 from official Python Language website
https://python.org
2) Install the following dependencies via pip:

i. Tensorflow
pip install tensorflow
ii. Numpy
pip install numpy
iii. SciPy
pip install scipy
iv. OpenCV
pip install opencv-python
v. Pillow
pip install pillow
vi. Matplotlib
pip install matplotlib
vii. H5py
pip install h5py
viii. Keras
pip install keras
ix. ImageAI
pip3 install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.2/imageai-2.0.2-py3-none-any.whl
· Create a python file with any name you want to give it, for example "FirstTraining.py".

· Copy the zip of the IdenProf dataset into the folder where your Python file is. Then unzip it into the same folder.

· Then copy the code below into the python file (e.g FirstTraining.py).

That's it! That's all the code you need to train your artificial intelligence model. Before you run the code to start the training, let us explain the code.

In the first line, we imported ImageAI's model training class. In the second line we, created an instance of the model training class. In the third line, we set the model type to ResNet (there are four model types available which are SqueezeNet, ResNet, InceptionV3 and DenseNet). In the fourth line, we set the data directory (dataset directory) to the folder of the dataset zip file you unzipped. Then in the fifth line, we call the trainModel function and specified the following values:
* number_objects : This refers to the number of different types of professionals in the IdenProf dataset.
* num_experiments : This is the number of times the model trainer will study all the images in the idenprof dataset in order to achieve maximum accuracy.
* Enhance_data (Optional) : This is to tell the model trainer to create modified copies of the images in the IdenProf dataset to ensure maximum accuracy is achieved.
* batch_size: This refers to the number of images the set that the model trainer will study at once, until it has studied all the images in the IdenProf dataset.
* Show_network_summary (Optional) : This is to show the structure of the model type you are using to train the artificial intelligence model.

Now you can start run the Python file and start the training. When the training starts, you will see results like the one below:

=====================================Epoch 1/100279/280 [===========================>..] - ETA: 1s - loss: 2.3097 - acc: 0.0625Epoch 00000: saving model to C:\Users\User\PycharmProjects\FirstTraining\idenprof\models\model_ex-000_acc-0.100000.h5


 1/280 [>.............................] - ETA: 52s - loss: 2.3026 - acc: 0.2500
Total params: 23,608,202


280/280 [==============================] - 51s - loss: 2.3095 - acc: 0.0600 - val_loss: 2.3026 - val_acc: 0.1000

Trainable params: 23,555,082

Non-trainable params: 53,120

______________________________________

Using Enhanced Data Generation

Found 4000 images belonging to 4 classes.

Found 800 images belonging to 4 classes.

JSON Mapping for the model classes saved to C:\Users\User\PycharmProjects\FirstTraining\idenprof\json\model_class.json

Number of experiments (Epochs) : 200 2/280 [>.............................] - ETA: 52s - loss: 2.3026 - acc: 0.2500 3/280 [>.............................] - ETA: 52s - loss: 2.3026 - acc: 0.2500

Let us explain the details shown above:

1. The statement " JSON Mapping for the model classes saved to C:\Users\User\PycharmProjects\FirstTraining\idenprof\json\model_class.json" means the model trainer has saved a JSON file for the idenprof dataset which you can use to recognize other pictures with the custom image prediction class (explanation available as you read further).

2. The line Epoch 1/200 means the network is performing the first training of the targeted 200
3. The line 1/280 [>.............................] - ETA: 52s - loss: 2.3026 - acc: 0.2500 represents the number of batches that has been trained in the present experiment
4. The line Epoch 00000: saving model to C:\Users\User\PycharmProjects\FirstTraining\idenprof\models\model_ex-000_acc-0.100000.h5 refers to the model saved after the present training. The ex_000 represents the experiment at this stage while the acc0.100000 and valacc: 0.1000 represents the accuracy of the model on the test images after the present experiment (maximum value value of accuracy is 1.0). This result helps to know the best performed model you can use for custom image prediction.

Once you are done training your artificial intelligence model, you can use the " CustomImagePrediction " class to perform image prediction with you're the model that achieved the highest accuracy.

Just in case you have not been able to train the artificial intelligence model yourself due to lack of accessing an NVIDIA GPU, for the purpose of this tutorial, we have provided an artificial intelligence model we have trained on the IdenProf dataset which you can use right now to predict new images of any of the 10 professionals that is in the dataset. This model achieved over 79% accuracy after 61 training experiments. Click this link to download the model. Also, if you have not perform the training yourself, also download the JSON file of the idenprof model via this link. Then, you are ready to start recognizing professionals using the trained artificial intelligence model. Just follow the instructions below.

Next, create another Python file and give it a name, for example FirstCustomImageRecognition.py . Copy the artificial intelligence model you downloaded above or the one you trained that achieved the highest accuracy and paste it to the folder where your new python file (e.g FirstCustomImageRecognition.py ) . Also copy the JSON file you downloaded or was generated by your training and paste it to the same folder as your new python file. Copy a sample image(s) of any professional that fall into the categories in the IdenProf dataset to the same folder as your new python file.

Then copy the code below and put it into your new python file

View sample image and result below.

waiter  :  99.99997615814209 chef  :  1.568847380895022e-05 judge  :  1.0255866556008186e-05

That was easy! Now let's explain the code above that produced this prediction result.

The first and second lines of code above imports the ImageAI's CustomImagePrediction class for predicting and recognizing images with trained models and the python os class. The third line of code creates a variable which holds the reference to the path that contains your python file (in this example, your FirstCustomImageRecognition.py) and the ResNet model file you downloaded or trained yourself. In the above code, we created an instance of the ImagePrediction() class in the fourth line, then we set the model type of the prediction object to ResNet by calling the . setModelTypeAsResNet() in the fifth line and then we set the model path of the prediction object to the path of the artificial intelligence model file ( idenprof_061-0.7933.h5) we copied to the project folder folder in the sixth line. In the seventh line, we set the path of the JSON file we copied to the folder in the seventh line and loaded the model in the eightieth line. Finally, we ran prediction on the image we copied to the folder and print out the result to the Command Line Interface.

So far, you have learnt how to use ImageAI to easily train your own artificial intelligence model that can predict any type of object or set of objects in an image.
https://github.com/OlafenwaMoses/ImageAI
You can find all the details and documentation use ImageAI for training custom artificial intelligence models, as well as other computer vision features contained in ImageAI on the official GitHub repository.

If you find this article helpful and enjoyed it, kindly give it a clap. Also, feel free to share it with friends and colleagues.

Do you have any questions, suggestions or will like to reach to me? Send me an email to guymodscientist@gmail.com . I am also available on twitter via the handle @OlafenwaMoses and on Facebook via https://www.facebook.com/moses.olafenwa .

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Train Image Recognition AI with 5 lines of code

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In this article, we will briefly introduce the field of artificial intelligence, particularly in computer vision, the challenges involved, the existing modern solutions to these challenges and how you can apply these solutions conveniently and easily without taking much time and effort.

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When we use “JSON Mapping for the model classes saved to C:\Users\User\PycharmProjects\FirstTraining\idenprof\json\model_class.json” it means that a JSON file was saved for the idenprof dataset which then can be use to recognize other pictures with the custom image prediction class.

The line Epoch 00000: saving model to C:\Users\User\PycharmProjects\FirstTraining\idenprof\models\model_ex-000_acc-0.100000.h5 refers to the model saved after the present training. The ex_000 represents the experiment at this stage while the acc 0.100000 and val acc: 0.1000 represents the accuracy of the model on the test images after the present experiment (maximum value value of accuracy is 1.0). This result helps to know the best performed model you can use for custom image prediction.