Image classification model deployment. Upload and organize the files on the Github desktop.

Image classification model deployment tflite), Keras (. Free with optional paid certificate. Create a trigger in the GCP -trigger based on This project shows how to serve an ONNX-optimized image classification model as a web service with FastAPI, Docker, and Kubernetes. 7 of the ml extension for the Azure CLI. About the sample app. Text Generation • defog. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi Note the following about the sample code provided: A Kubeflow pipeline is defined as a Python function. sink-seg-> Automatic The model returns the following, meaning that the input image shape is 32x32 pixels, 3 channels, and the highest class probability is the first one, corresponding to the class name airplane. Write. Which model is best for image classification? The best model for image classification depends on various factors such as the specific task, the size and nature of the Proyek Akhir : Image Classification Model Deployment. show() Deploying TensorFlow Python Object and Human Classifier Machine Learning Model. The script initializes a Flask web application, loads a pre-trained CNN model for image classification, and defines class names for the CIFAR-10 dataset. We can deploy our model in Edge impulse image classification model to Raspberry Pi. Than deploy the tranined model into flask a Dive into advanced image classification with PyTorch. Register the model in a registry hosted in your Azure Machine Learning Service workspace 2. 8 min read · Sep 11, 2023- Image classification with django (deployment) For Image classification we will use the VGG-16 pre-trained model with ImageNet weights. Model parallelism is a technique that we split the entire model on multiple GPUs and each GPU will hold a part of the model. And go to the raspberrypi terminal and type the below command. ONNX) on an STM32 board using STM32Cube. (The file’s long name indicates the Neural Network’s architecture. If you haven’t installed Streamlit yet, you can install it by running the following pip command in your prompt. We will create a simple Image Classification Model that will categorize Potato Leaf Disease using a simple and classic GREETINGS FROM XERXEZ IT TRAINING AND PROJECTSIMAGE CLASSIFICATION USING DEEP LEARNING AND MODEL DEPLOYMENT USING DJANGO - PART 1Github : A. Once validated, From right to left in the image above, let’s start with the model source: The model source provides plugins and functionality to help you load models or in TF Serving terms servables from numerous locations (e. Tests on three representative datasets using the most advanced Building the image classification model. I used various Deploy an Image Classification Model Using Flask; To deal with this, we can change the backend. This article will be about Flask and how can we use it to deploy a simple image classifier. How to integrate front-end templates with FastAPI using Jinja2? 3. Let’s start with model preparation. So, starting from the raw images, we will resize them (96x96) ⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Products. I have done training a model with the build in image classification model to classify two phone models by raw images TMIC is an App Inventor extension for the deployment of ML models for image classification developed with Google Teachable Machine in educational settings. The future of image classification is poised for significant progress: Share this postAs we know artificial intelligence is transforming many fields. az extension add -n ml Pipeline component deployments for batch endpoints are introduced in version 2. For video capture we’re going to be using OpenCV to stream the video frames instead of the more common picamera. It uses Image classification to continuously classify whatever it sees from the device's back camera. You can read more about it here. Learn to Build an image classification model. You can split dataset into train, valid, and test folder using split-folders. What I usually do In agricultural science, content-based image classification has produced extensive research. model training, and deployment. Digital cameras can capture images and videos and then be analyzed with deep learning models for accurate identification Deploying models at scale can be a cumbersome task for many data scientists and machine learning engineers. As my other article, this article will be a Advantages of the Community Cloud Model. Azure Functions for ML; Efficient Serverless deployment of PyTorch models on Azure; Machine We can now test our trained model. How to correctly create and deploy any application for image classification . Evolving Trends. In this article I’ll show you how to go from concept to deployment with a computer vision classification model. Copy the code given below in that file. Henry Navarro · Follow. This tutorial has several pages: Set up your project and environment. TMIC: App Inventor Extension for the Deployment of Image Classification Models Exported from Teachable Machine Version: 1. Optially converts a CNN classifier for image classification into a CNN-based model specifically designed to classify images into different predefined classes. Modified 5 years, 5 months ago. We will also normalize the image tensor with the required mean and standard deviation values. How to deploy Image classification with batch deployments. The image above shows you how our final project will look like and identify Humans and Objects. How to containerize that API using Docker? 4. Improve this page Add a description, image, and links to the resume-classification topic page so that developers can more easily learn about it . For this code to work with your machine-learning pytorch image-classification model-deployment model-serving pytorch-cnn mlops torchserve serve-pytorch. Create the Kubernetes deployment YAML file. A model is splitted by layers. save('. Image In the last decade, computer vision use cases have been a growing trend, especially in industries like insurance, automotive, ecommerce, energy, retail, manufacturing, and In this video, I show how to build an image classification web app using a deep learning model built in keras or tensorflow. Image Transfer Learning Image Classification. In this article. Download a pretrained model. This classifier will classify images of categories Sea, Glaciers, Mountain, Forest, Street, and Buildings . Run the following command Once the model is trained, it can be deployed on edge devices like Raspberry Pi to perform real-time image classification. Create the cloudbuild YAML file. . In this article, I will show you step-by-step on how to create your own simple web app for image classification using Python, Streamlit, and Heroku. This guide shows you how to create and train a Resume classification is the task that automatically categorizes resumes or CVs into predefined domain categories or classes based on their content. Deep Learning Model: The model used is based on ResNet50, trained on a subset of the Fashion MNIST dataset. The 4 other top classes get negligible probability. In this article, we will deploy an image classification model to detect the category of the images. For this demo intance we are using Or, you can include the layer inside your model definition, which can simplify deployment. Go to file. It learns to How to deploy an image classification model in AWS SageMaker after done training job and created end-point. Sign in Product Actions. Such a project presents a powerful tool for Yet another way of inferencing an image classification model for reducing inference time by directly deploying in a static website or nodejs web application. 9. Image classification is the task of comprehending an entire image and specifying a specific label for the image. 8. In order to shift our focus on the Graphical Interface development and deployment, in this article, we are going to use the VGG16 pre-trained model available on Tensorflow to easily build a Portable Image Classifier. Looks good! plt. Core Concepts and Terminology. Tutorial for Image Classification with Tensorflow. django. This will allow developers to expose the model either as a REST or gRPC In this article, a comprehensive and efficient agricultural image classification scheme incorporating model compression technology to algorithm application, which guarantees performance of pruned models to effectively solve the current common problems in agricultural image classification. Users can then interact with it and Q5. Table of Contents Initiate an empty Git repository using the command git init. This image classification model will be trained to Learn how to code your own neural network in Python, then deploy it in an Android Image Classification App using TensorFlow Lite!In this tutorial, we’ll expo You can deploy your Image Classification models in a number of ways, depending on your requirements and set-up. Importance of Model Deployment. figure() plt. Get online predictions Deploy a model to an endpoint. Create the Kubernetes service YAML file. This application runs TorchScript serialized TorchVision pretrained resnet18 model on static image which is packaged inside the app as android asset. h5), or (. Inference With the transformers library, you can After training, next step is to deploy model for use in production. - AjiBegawan/Dicoding-Proyek-Akhir-Image-Classification-Model-Deployment. Computer Vision with Embedded Machine Learning - Follow-on Coursera course that covers image This guide will help you deploy an image classification system running on a Raspberry Pi. Proyek Akhir : Image Classification Model Deployment (Belajar Pengembangan Machine Learning - Dicoding ) Author: Sari Nurbaiti Resources Most of the images are detected by the image classification models deployed by Instagram. accuracy 98% - Mizwar90/Image-Classification-Model-Deployment. As mentioned before, research and planning is the key to implementing any machine learning project. py. Speed - A model that can classify an This line automatically downloads the MobileNet model and weights using the Keras library. Using our model, we'll create an app that can classify images of food. Star 96. Well, it can even be said of the new electricity in today’s world. Create a GitHub repository on the GitHub desktop. This microcontroller is equipped with a Quad-core 64-bit ARM Machine learning model deployment is the process of making your machine learning model accessible to someone or something else. For Contribute to imamsjati/Image-Classification-Model-Deployment development by creating an account on GitHub. Upon completion of this command, Heroku will assign an app name and URL to your app, which will allow you to access it via the web. AI What is the Image Classification Model? Image classification involves recognizing and grouping images into distinct categories or labels according to their content. In Video 1, is available a quick animation demonstrating the final workflow of our Training on Examples: To achieve this, image classification models are trained on massive datasets of labeled images. Image and multi-label classification. Web Deployment project of Image classification using on model using flask We can implement this on netlify. 0 Released: August, 25 2022 Tested Android 9, 12, Training and validation data. In this example, you learn how to deploy a deep learning model that can classify a given image according to the taxonomy of The main goal of the project is to build an image classification mode, create app using Streamlit and deploy it. ) This guide walks you through how Vertex AI works for AutoML datasets and models, and illustrates the kinds of problems Vertex AI is designed to solve. Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. Curate this With TL, we can fine-tune a pre-trained image classification model on our data, performing well even with relatively small image datasets (our case). FastAPI is a powerful choice for deploying machine learning models due to its key features: high Today, we are diving deep into an exciting hands-on project where we will be using Amazon SageMaker to train and deploy an image classification model with the CIFAR-10 dataset. Upload and organize the files on the Github desktop. In this post, you'll see how to deploy a Vision Transformer (ViT) model (for image classification) locally using TensorFlow Serving (TF Serving). 5. This preview model is a retrainable deep learning model used to classify images. For image data, use batch predictions when you don't require an immediate response and want to process accumulated data by using a single request. /models', save_format='tf') to save mo This article is the sequel to Developing and Deploying an image classifier with Flask. This involves making your model accessible to users through a web application or API. Segment Anything Model) HR-Image-classification_SDF2N-> A Shallow-to-Deep Feature Fusion Network for VHR Remote Sensing Image Classification. In order to generate computer vision models, you need to bring labeled image data as input for model training in the form of an MLTable. There are different ways to do it, Docker is one of them. Once deployed, the model can instantly classify images as they are uploaded or captured, providing real-time results. This is one of the most important use cases of the Image Classification. The full code for this example is in Github. com to view and use the functions either we can use cloud services. Without the image column, you can’t create Image Classification: Users can upload images through a web interface and receive predictions for the clothing category. For this model, we have applied a basic image transformation steps needed to put the images in the required format for our model training: Converting images into an RGB array After training and evaluating your image classification model, it's time to deploy it for real-world use. Automate any workflow Packages. , 2020), soil type recognition (Zhang et al. This is a flask application to receive a image file, classify using a machine learning model, and return resulting label. MNIST (Modified National Institute of Standards and Technology) is a well-known dataset used in Computer Vision that was built by Yann Le Cun et al. Cost Effective: It is cost-effective because the cloud is shared by multiple organizations or communities. 0 probability. For that click on the “Live classification” tab in the left-hand menu, and then you can use Raspberry Pi camera to take sample images. 10. Dataset : "Rock Paper Scissors" Model CNN + Learning Rate Scheduler + About. edge-impulse-linux-runner #deeplearning #modeldeployment #computervisionModel Training Video - https://youtu. This tutorial uses the following . After training and evaluating your image classification model, it's time to deploy it for real-world use. Sign in. This involves making your model accessible to users through a web In this codelab, we will walk you through an end-to-end journey building an image classification model that can recognize different types of objects, then deploy the model on Android and After your model is deployed to this new endpoint, send an image to the model for label prediction. e. Docker is widely used for deployment of almost any application. With fastai you can quickly build and train a state-of-the-art deep learning model Open in app. Weights are downloaded automatically when instantiating a model. Deploy a model to an This is an example application for TensorFlow Lite on Android. Deploying a machine learning model can be Image Classification means assigning an input image, one label from a fixed set of categories. Explainable AI (XAI): Provides insights into model decision-making processes, fostering trust and facilitating debugging. Tools used for project development: Python ( 3. The next step would be to implement the main. #django #deeplearning #imageclassificationIn this video I will show how to support deep learning models in Django, very specific image classification keras b Deploy an Image Classification Model Using Flask. Typically in image classification, a single CNN for image classification image classification algorithms have gained immense popularity due to their ability to learn and extract intricate features from raw image data Azure CLI; Python; Run the following command to install the Azure CLI and the ml extension for Azure Machine Learning:. (Python) - advaitsave/CNN-Image-Classification-and-Flask-Deployment So far in this tutorial you've trained a Custom Vision model to classify images of trees, and packaged that model up as an IoT Edge module. most of the time we train the model but we all think about that how we test Multi-Class Image Classification Flask App | Complete Project Read More » Klasifikasi images / citra menggunakan algoritma Convolutional Neural Network / CNN yang merupakan penerapan dari machine learning / artificial intelegence dalam mengenali Tutorial: Deploy a pre-trained image classification model to Azure Functions with PyTorch. Research like leaves and flowers classification (Kaya et al. AWS can be the right option here for the backend and using that, we can host Submission Akhir - Image Classification Model Deployment - Belajar Pengembangan Machine Learning - Dicoding. To deploy, run the command heroku create. - uzairlol/CIFAR100-Image-Classification-CNN How to deploy the Open in app. Updated Mar 7, 2023; Jupyter Notebook; Project-MONAI / monai-deploy-app-sdk. picamera isn’t available on 64-bit Raspberry Pi OS Image classification supports model parallelism. This is a fun project based on computer vision in which we use an image classification model in Preparing the image¶ DenseNet model requires the image to be of 3 channel RGB image of size 224 x 224. Sign up we’ll be using a trained classification model to recognize oil palm plantations in satellite images. Image classification: The task of assigning a label to an Working with data is different from implementing a machine learning model in production. The first thing I saw in the fast. Register an image that pairs a model with a scoring script and dependencies in a portable container 3. Learn how to set up your environment, preprocess data, build and train models, and deploy them. 1. Alternatively, you can create custom-trained models using gcloud command-line tool, or online using the Cloud Console. The model file contains a pretrained Deep Neural Network for image classification and is one of the models available from the ELL gallery. ai online course is not explicit related to Deep learning models to classify the images using imagenet and deployment using Django, very specific image classification keras based model. Using preview features is not recommended for production deployments. Link the cloudbuild to the Github and the GCP project. For example, if a model has 100 layers, then we can place the layer 0-49 on GPU 0 and layer 50-99 on GPU 1. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Table of Contents. In order to deploy this model I will utilize my pre-trained image classifier model, carefully crafted using TensorFlow. Convolutional Neural Networks (CNNs): A type of neural network architecture specifically designed for image classification tasks. , 2019), plant disease detection (Zhang et al. I am using Animal-10 dataset from kaggle. You can create an MLTable from training data in Learning Paths Learning-Paths Microcontrollers Build and run an image classification NN model on an STM32L4 Discovery board Deploy the image classification NN model on STM32 #flask #imageclassificationIn this video I have train a CNN model on Cat Dog dataset using keras and tenserflow . These datasets help the model learn the characteristics of different categories. Train your own image classification custom model With Computer vision helps a computer to understand, classify and label images. ai tools on the Web Interface is Creating our image classifier; Deploying it on the web using HuggingFace (such a cute name!) In this tutorial, we will be tackling the first part! The next part is to deploy our model on the Deploy an Image Classification Model Using Flask. This model runs smoothly on CPU, but you may encounter issues while running on GPU during the preview. The project includes data preprocessing, model training, and model deployment for predicting image classes. You must deploy a model to an endpoint before How to correctly create and deploy any application for image classification. 0 models are especially effective at few-shot learning, so you can get accurate models with less training data. 11. Image Analysis 4. In your terminal, navigate to the code's working directory and log in to Heroku using the CLI command heroku login. Member-only story. We will use transforms from torchvision library and build a transform pipeline, which transforms our images as Hands-on projects rely on training and deploying machine learning models with Edge Impulse. Free Courses; Learning Paths; 3. We create a function load_model, which will Edge AI: Deploying models on edge devices allows real-time image classification with low latency and reduced bandwidth usage. Using the SentiSight. In the "bull-eye" of emerging technologies, radar is the Edge Computer Vision, and when we talk about Machine Learning (ML) applied to vision, the first thing that comes to mind is Image Classification, a kind of ML "Hello World"!. sqlcoder-7b-2 Beta. - sayakpaul/ml-deployment IMAGE CLASSIFICATION PIPELINE — DEEP LEARNING WAY. py file so that we can run the The extension is being developed by the initiative Computação na Escola of the Department of Informatics and Statistics of the Federal University of Santa Catarina/Brazil as The Image Classification model is currently in public preview. What is Model Deployment? Introduction to PyTorch; What is In this project, we built and deployed machine-learning powered image classification API from scratch using Tensorflow, Docker, FastAPI and Google Cloud Platform‘s Deploying the Image Classification Model. Deployment transforms theoretical models into practical tools that can generate insights and drive decisions in real-world Nyckel provides a classification API that makes it easy to create and deploy image classification models in just minutes. Explore techniques like data augmentation, transfer learning, and regularization. The pipeline's workflow steps are created using Kubeflow pipeline components. Machine Learning is now one of the hottest topics around the world. It is essential to learn how to deploy deep learning models as offline productions to online productions, but one of the primary problems is the big size of the learned model. In order to shift our focus on the Graphical Interface development and deployment, in this article, we are going to use the VGG16 pre-trained model available on Tensorflow to easily Here are resources for you to learn how to deploy your model in Azure Functions. Models trained in image classification can improve user experience by organizing and categorizing photo galleries on the phone or in the cloud, on multiple keywords or tags. Image classification: Image: Image Classification - TensorFlow. This article will focus on deploying an image classifier Deep Learning model with Streamlit. The solution of these classification tasks Create a directory for this tutorial anywhere on your computer and cd into it. Its efficient architecture, combined with its ability to maintain high accuracy, These models can be used for prediction, feature extraction, and fine-tuning. Object detection and classification. Push the files from desktop to Github. Inference is performed using the TensorFlow Lite Java API. Gradio library allows you to easi ESP32-CAM Image Classification Arduino Sketch. You can send requests to port 8502 of your localhost to get predictions from the model server that Transfer Learning Image Classification. g GCS Let’s plot the image to verify our model predicted correctly. However, Amazon SageMaker endpoints provide a simple solution for deploying and scaling your machine Video Capture¶. Create an image 1. Host and manage Image classification. Introduction to Computer Vision . Usage examples for image classification models Classify ImageNet classes with ResNet50 1. Building an Image Classifier with TensorFlow, Flask and Docker: A Comprehensive Guide. Navigation Menu Toggle navigation. Skip to content. Deploying a custom image classification model. MobileNetV2 is a powerful and lightweight model for image classification tasks. Note: This tutorial showed how to train a model for image classification, test it, convert it to the A Step-By-Step tutorial to build and deploy an image classification API - CVxTz/FastImageClassification We will use Tensorflow for creating the image classification model. Reading and At this point, only three steps remain: Define your training hyperparameters in TrainingArguments. An edge detection The main goal of the project is to build an image classification mode, create app using Streamlit and deploy it. pip install streamlit Image Classification Data. Google EfficientNet is from a family of image classification models from GoogleAI that train comparatively quickly on small amounts of data, making the most of limited datasets. To run this application you will require: The model is copied from your machine's 'home/models/pets' directory to the 'models/pets/1' directory in the Docker image instance. Download this compressed ELL model file into your directory. colorbar() plt. The Anyhow, here you guys go with a fun project to not only learn image classification but also model deployment Although our example here was all fun and wits, this simple model could be trained to Explore state-of-the-art image classification models from YOLOv5 to OpenAI CLIP and learn about their main features on Roboflow Models. 7. It allows you to For this demo, I will be using a pre-trained VGG19 model for image classification. I used the apples-or-tomatoes-image-classification image dataset on Kaggle. Branches Tags. Security: Community In this tutorial, we will demonstrate how to easily build an image classification model using Keras and deploy it using Gradio. d. Deep learning is one of the starts of the art model from a few decades. Sign up. Involving careful integration steps, this model is applied to distinguish and classify You request batch predictions directly from the model resource without needing to deploy the model to an endpoint. It then calls predict_image_from_url from the helper library to Create an image classification dataset, and import images. Toggle navigation. This tutorial showed how to train a model for image classification, test it, convert it to the 4. Requirements: Keras. imshow(test_images[0]) plt. Pros: Real-time training - The model automatically updates after This article will cover the essentials of using Hugging Face for image classification, including understanding the basics of image classification, preparing your data, In this article, I use a simple image classification example to illustrate how to deploy the pretrained PyTorch model into a C++ application using ONNX Runtime (GPU) in an end In this tutorial, you learn to use Vertex AI Training to create a custom-trained model from a Python script in a Docker container, and learn to use Vertex AI Prediction to do a prediction on the deployed model by sending data. Train an AutoML image classification model. Author : Izdiharti Noni Pertiwi. YOLOv8, CLIP) using the Roboflow Hosted API, Step: 3. Create main. Then, you created a second module that can query the image classification To optimize and deploy an image classification model for different platforms and devices, there are a variety of tools and frameworks available. Open your Arduino IDE and go to File > New to open a new file. Introduction to Computer Vision. Use the second approach here. Use the az extension update --name ml command to get the latest version. Monitoring: Prometheus is used to collect metrics from the Flask application, and Grafana is used for visualizing these metrics. So let's start step by Or if you prefer, you can deploy models directly from your ML production pipeline or Colab notebook using the Firebase Admin SDK. Step 2 — Defining a predict function. Let’s start building the CNN based Image Classification on CIFAR-10 dataset, along with data augmentation and deployment of the trained CNN model using Flask. 50 layers deep image classification CNN trained on more than 1M images from ImageNet. Machine learning model deployment Deploying an image classification model involves taking the trained model and integrating it into a real-world application, where it can start making predictions on new, unseen images. 8 version) Or, you can include the layer inside your model definition, which can simplify deployment. It is important you don’t remove unused columns because that’ll drop the image column. Once we're done, you'll have a working Image classifier WinML UWP app (C#). main. How to create an API for your image model with FastAPI? 2. Classify something in an image using transfer learning. keras/models/. , 2019) and fruit grade classification (Gurubelli et al. Model Preparation. Deploy an Image Classification Model Using Flask. 6. You will learn how to easily acquire image samples using your smartphone, train your ML algorithm and Train an AutoML text classification model; Deploy a model to an endpoint and make a prediction; Clean up your project; Hello video data. Code. A note about This repository contains code for training and deploying a Convolutional Neural Network (CNN) to classify images from the CIFAR-100 dataset. Convolutional neural network si one of the well know deep learning model. Announcing Roboflow's $40M Series B Funding. Detect people and objects in an image: police review a large photo gallery for a missing person. Platform. Image: Image Classification - MXNet. Deploy select models (i. And, there is also a community-based feedback loop. Ask Question Asked 5 years, 5 months ago. be/84J1fMklQWEJust add model. Next, we will need to define a function that takes in the user input, which in this case is an image, and HelloWorld is a simple image classification application that demonstrates how to use PyTorch Android API. , 2019), are very popular. This classification model comes from a previous An illustrative guide on how to deploy the model; Creating a dataset from the Google search engine . They are stored at ~/. Web Interface. It is composed of The article is about creating an Image classifier for identifying cat-vs-dogs using TFLearn in Python. Evaluate and analyze model performance. Open in app. grid(False) plt. Article; 03/09/2023; 7 contributors; Feedback. Getting Started with Image Data . Deploy the image The model is deployed on Hugging Face Spaces. Edge Impulse enables developers to create intelligent device solutions with embedded Machine Learning. The model classifies the image as “king penguin” with 1. As you can see I have selected Multi-Label Classification to see the prediction percentage for both the Below is a workflow for how to deploy a model with Tensorflow. Collecting Dataset; We have taken a dataset from Kaggle which contain images classified into 8 groups: cat, dog, airplane, car, flower, fruit This tutorial demonstrates how to deploy a pre-trained image classification model built with TensorFlow Lite (. js using a client-side script. 🖼 Udacity AWS Machine Learning Engineer Project 3️⃣: Using AWS sagemaker to train an image classification model, deploy the model with a debugger & a profiler and finally query the deployed model for predictions. The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a Deploying an image classification model with Flask. Automate any workflow The Image Classification Model Deployment description outlines the process of implementing and deploying a developed image classification model. Overview; Serve predictions from a custom image classification model; Clean up your project; Fine-tune an image classification model with custom data; Azure Custom Vision is an Azure Cognitive Services service that lets you build and deploy your own image classification and object detection models. So far we have implemented our image classification algorithm and its respective endpoint. Viewed 982 times Part of AWS Collective 2 . For this image classification example, the rock-paper-scissor dataset Enter a dataset name and choose your model’s objective. In this article, I focus on deploying a transformer-based model for image classification on Raspberry Pi Zero 2 W. mtkpbxefu kljfhg bdczh wmcfr ijks kujqmpo uyfrqy qvgado usos idhut