Teachable Machine iPad-iPhone Classifier

@softsolution2000

I understand you need a lightweight image-classification prototype to separate iPads from iPhones with over 90% accuracy using Google's Teachable Machine. I will use a mixed set of photos and practical tricks like augmentation and transfer learning to achieve this. The workflow will include uploading and labeling images, choosing Teachable Machine options, and exporting the model with a simple walkthrough for non-developers. I am confident in delivering this project within your budget and timeframe. Please review my profile for relevant experience. Let's discuss the details and get started.

@OlektraGroup

Hi. You need to bridge the gap between a Teachable Machine model and a native iOS environment, likely requiring a conversion to CoreML for optimal performance on iPad/iPhone. I’ve handled this specific migration before, notably converting YOLOR and HuggingFace models to TFLite and ONNX for mobile deployment. My approach involves exporting your Teachable Machine model, running it through CoreML Tools to optimize the graph, and integrating the resulting .mlmodel file into your Swift/Xcode project. I’ve previously delivered similar mobile-ready vision pipelines, including a Flutter/TFLite project that required precise on-device inference optimization. I won't waste time on boilerplate; I’ll get your model running natively on the device hardware immediately. Are you planning to run the inference continuously via the camera feed, or will this be a trigger-based snapshot classifier?

@ixorawebmob

Hello, I’ve gone through your project details and this is something I can definitely help you with. With over 10 years of experience in machine learning and image processing, I specialize in developing lightweight prototypes that are user-friendly and effective. I focus on clean workflows that allow for easy demonstration to non-technical stakeholders, making your project a perfect fit. I will leverage Google’s Teachable Machine to create an image classifier that accurately distinguishes between iPads and iPhones, incorporating practical techniques like data augmentation and transfer learning to ensure we hit the 90% accuracy benchmark on fresh images. In addition to the model, I’ll provide a concise walkthrough, complete with screenshots or video, detailing the image upload process, the choices made in Teachable Machine, and guidance on exporting and testing the model. Here is my portfolio: https://www.freelancer.in/u/ixorawebmob I’m eager to get started! Could you clarify: 1. What is your expected timeline for completion? Let’s discuss over chat! Regards, Arpit Jaiswal

@nrajuu07

As a seasoned Machine Learning Engineer specializing in Computer Vision, I'm confident that I can deliver the image classification prototype you need using Google's Teachable Machine. My extensive experience in medical image analysis will be highly advantageous for your project. Working with a variety of medical imaging data including MRI and CT scans, I've developed sophisticated models in deep learning such as ResNet, EfficientNet, and UNet which can definitely cater to your need for classifying iPads from iPhones. Moreover, my proficiency in Python ML pipelines, familiarity with TensorFlow and practical know-how of augmentation techniques will be instrumental to ensure better than 90% accuracy on unseen images. Also, transfer learning can be effectively brought into play to simplify the workflow without compromising performance. Beyond delivering the prototype, I place great emphasis on documentation and knowledge transfer. I commit to providing you with a concise yet comprehensive walkthrough elucidating all steps from image uploading to label selection in Teachable Machine as well as exporting the model and conducting quick tests. This will equip your non-technical stakeholders to confidently repeat or demo the process. Rather than leaving you with a mere prototype, you'll be bestowed with a fully functional, documented machine learning system ready to serve your needs.

@rbtech1984

Hello There!!! ★★★★ (Build a simple Teachable Machine model reaching 90%+ accuracy for iPad vs iPhone) ★★★★ I understand you need a lightweight image classifier using Teachable Machine to clearly separate iPads and iPhones, with strong accuracy on unseen images and a simple walkthrough for non-tech users. ⚜ Clean dataset upload & labeling in Teachable Machine ⚜ Image augmentation for better generalization ⚜ Class balancing & validation split setup ⚜ Model training with optimal settings ⚜ Accuracy testing on hold-out images ⚜ Export to TensorFlow/Python ready format ⚜ Simple Loom-style walkthrough guide I have experience in ML and computer vision, worked on similar classification tasks with high accuracy results. I enjoy making models simple and demo-friendly. I will use Teachable Machine with transfer learning, proper splits and tuning to achieve your goal without overcomplicating. Would love to start once you share dataset, lets discuss timeline. Warm Regards, Farhin B.

@abdf2010

Hey, your project, Teachable Machine iPad-iPhone Classifier looks like a great fit for my skills. I've worked on similar Python projects and can deliver solid results. Let me know if you'd like to chat about the approach.

@letswork92

I understand that you need an effective solution for accurately classifying images of iPads versus iPhones, leveraging Google’s Teachable Machine. Achieving over 90% accuracy on unseen images is crucial for your demo to non-technical stakeholders. With my 12+ years of experience in full-stack development and machine learning, I can efficiently implement image augmentation and transfer learning strategies to enhance performance without complicating the workflow. Using platforms like Flutter or React.js for any additional application needs ensures a seamless user experience. To ensure clarity for your team, I'll provide a clear walkthrough with screenshots or a Loom video covering image uploads, option selections in Teachable Machine, and model export processes. This will empower non-developers to replicate the steps confidently. Could you share how many initial images you'll provide? This will help tailor the training process effectively.

@jagratip3

Hi there, I can help you build a lightweight Teachable Machine image classification prototype to distinguish iPads from iPhones with a clear, demo-ready workflow. Approach: • Prepare your dataset into balanced classes (iPad vs iPhone) with diverse angles, lighting, and backgrounds to improve generalization. • Train a MobileNet-based image model in Teachable Machine using transfer learning and built-in data augmentation (flip, zoom, brightness). • Tune training settings (epochs, batch size, validation split) and iterate until we achieve ≥90% accuracy on a hold-out set. • Validate the model with unseen images and refine if misclassifications appear. • Export the model (TensorFlow/Keras/TFLite) for optional integration into Python or other pipelines. • Provide a simple walkthrough (screenshots or Loom-style video) showing dataset upload, labeling, training configuration, testing, and export so non-technical users can reproduce and demo it easily. Deliverables will include the Teachable Machine project/export, documentation, and brief suggestions to further improve accuracy if needed. Ready to begin as soon as you share the image dataset. Best Regards JP

@shibbuquadir

We’ll create an image-classification prototype that does much more than merely distinguish iPads from iPhones—it'll do so with an accuracy of over 90%. Harnessing the power of Google's Teachable Machine and our proficiency in React Native and Python, we shall develop a lightweight, sleek model that allows for live demonstration without compromising on exporting capabilities for TensorFlow or a Python pipeline. In addition to leveraging your curated dataset encompassing varied angles, lighting, and backgrounds, we'll deploy different techniques such as augmentation and class-balance tweaks to meet the performance benchmark while ensuring a streamlined workflow. Our work is not just about building the model- it’s about knowledge transfer too. We’ll provide you with a concise yet comprehensive Loom-style walkthrough, carefully explaining each step in a way that non-technical stakeholders in your team can easily understand. With many successful projects under our belt, we understand that quality deliverables aren’t limited to the finished product. That's why even after delivering a Teachable Machine project link or .zip export that achieves ≥90 % accuracy on your hold-out set and equipping you with an easy-to-replicate walkthrough guide, we won't leave you hanging. We’ll also provide you with further ideas to enhance accuracy if you choose to expand beyond the current scope.

@cindyviorinar

Hi there, I read your brief and I’m confident I can build a lightweight Teachable Machine classifier that reliably separates iPads from iPhones and hits >90% accuracy on unseen images. I’ll use your mixed-device photos plus curated stock images, apply balanced class sampling, sensible augmentation (rotation, brightness, flip), and leverage Teachable Machine’s transfer-learning options for a compact, robust model. I’ll deliver a shareable Teachable Machine project link and a .zip export, plus a short Loom walkthrough showing uploads, settings chosen, and how to export and test. I can start as soon as you share the first image batch and will provide an initial prototype within 5 days; next I’ll run the hold-out test and iterate to reach the target. How many images per class will you provide for the initial batch, and do you already have a hold-out set reserved? Thanks, Cindy Viorina

@chiragpipal

With a keen focus on accurately classifying iPads and iPhones, I propose a lightweight image-classification prototype using Google’s Teachable Machine. Through 5 years of experience and similar projects offsite, I ensure clean data separation, achieving over 90% accuracy on diverse images. By utilizing practical techniques like augmentation and transfer learning, I guarantee a seamless workflow integrating Teachable Machine with TensorFlow or Python for further scalability. My approach emphasizes simplicity and quality, ensuring non-technical team members can easily replicate or present the model. Let's commence discussions to commence the project and achieve exceptional results efficiently. Chirag Pipal Regards

@billybryan98

Hello, I hope you’re well. I’m an independent developer with solid hands-on experience in computer vision and ML pipelines for mobile apps. I’ve built lightweight image-classification workflows, tuned data augmentation and transfer learning, and focused on keeping models simple and demonstrable so non-technical stakeholders can see the results clearly and export cleanly to TensorFlow or Python pipelines. For your iPad vs iPhone classifier, I’ll configure a Teachable Machine project, curate and augment your mixed image dataset, apply practical tricks to balance classes and stabilize accuracy, and aim for above 90% accuracy on hold-out data. I’ll deliver a ready project link or a .zip export, plus a concise walkthrough (screenshots or a Loom-style video) showing how images were uploaded/labelled, which Teachable Machine options I used and why, and how to export and test quickly. I’ll keep things simple so your team can replicate the demo. Please feel free to share any preferences for demo format or dataset constraints, Best regards, Billy Bryan

@PrateekP3003

Hello! As per your project post, you’re looking to build a lightweight image-classification prototype in Google’s Teachable Machine that cleanly separates iPads from iPhones, with the ability to demo live to non-technical stakeholders and export for further use in TensorFlow or Python pipelines. My focus will be on delivering a robust prototype, featuring: high-accuracy classification (≥90%) using your supplied device images, practical augmentation and class-balance techniques, clean labeling workflow, and a concise walkthrough showing how to upload images, configure Teachable Machine options, export the model, and run quick tests. I specialize in rapid AI prototyping and model deployment with clear, non-technical knowledge transfer. My focus will be on creating a demo-ready solution that meets your accuracy targets, is easy for your team to understand, and provides actionable ideas for further improvement. Let’s connect to review your image set, accuracy goals, and workflow so we can start building the prototype immediately. Best regards, Prateek

@jideo6

Hello Mate!Greetings , Good morning! I’ve carefully checked your requirements and really interested in this job. I’m full stack node.js developer working at large-scale apps as a lead developer with U.S. and European teams. I’m offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I’m well versed in React/Redux, Angular JS, Node JS, Ruby on Rails, html/css as well as javascript and jquery. I have rich experienced in Python, Computer Vision, Data Augmentation, Mobile App Development, iPhone, Machine Learning (ML), Image Processing and Objective C. For more information about me, please refer to my portfolios. I’m ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details.. Thanks

@OBRY8246

Hello, I’m excited about the opportunity to develop a lightweight image-classification prototype for distinguishing between iPads and iPhones using Google’s Teachable Machine. With your supplied images, I will ensure that the training set covers various angles, lighting, and backgrounds to create a robust model. My goal is to achieve over 90% accuracy on unseen images by employing effective techniques such as data augmentation, class-balance tweaks, and transfer learning, while keeping the workflow straightforward. I will also create clear walkthrough documentation, including screenshots or a short Loom-style video, to guide your team through image uploads, labeling, chosen options in Teachable Machine, and the model export process, ensuring they can demo it competently. Upon completion, I’ll provide a Teachable Machine project link or .zip export meeting the accuracy criteria, detailed documentation, and additional suggestions for potential accuracy improvements. What is your timeline for this project, and when can I start receiving the initial image batch? Best regards,

@Royalitservices

Hi there, You’re absolutely in the RIGHT PLACE. I’ve delivered SIMILAR PROJECTS multiple times and know EXACTLY how to execute this efficiently and correctly from day one. To lock down the SCOPE, TIMELINE, AND PRICING, I’ll need to ask you a few key questions. Unfortunately, Freelancer’s 1500 CHARACTER LIMIT doesn’t allow me to break everything down properly here. Let’s jump on CHAT so I can show you my PROVEN PAST WORK, walk you through the REAL RESULTS I’ve delivered, and outline a CLEAR ACTION PLAN for your project. You’ll immediately see why my approach is DIFFERENT and EFFECTIVE. If you’re serious about getting this done RIGHT, I’m ready to move forward. Looking forward to CONNECTING and WINNING TOGETHER. Cheers, Mayank Sahu

@ksaweryk4

Hello, Throughout all of my 7+ years as a Machine Learning Engineer and Python Backend Developer, I have focused on deploying intelligent solutions that drive tangible business impact. I have a strong proficiency in Python and specialize in end-to-end ML pipelines and LLM integrations, which aligns perfectly with your project needs for the Teachable Machine iPad-iPhone Classifier. Moreover, I'm well-versed in computer vision techniques, know how to leverage libraries like TensorFlow and PyTorch, and have experience using Google's Teachable Machine. What sets me apart is my ability to distill complex concepts into simplified explanations that a non-technical audience can easily understand. I understand the importance of knowledge transfer, and so I promise to provide you with a thorough walkthrough including screenshots or short videos if necessary. Furthermore, I'll share insights on any further accuracy-boosting ideas we could explore to go beyond the benchmark of 90% accuracy that you require, ensuring our prototype is not only effective but also future-proof. Having built AI diagnostic systems adhering to HIPAA regulations and ML-based fraud detection models among other projects, I am experienced in curating datasets and employing practical tricks to achieve optimal model performance. As a seasoned freelancer who values crisp communication and timely delivery, I will be able to efficiently manage this project while maintaining h Thanks!

@Apps1m1ty

Distinguishing iPads from iPhones in Teachable Machine (which uses MobileNet) often fails when images share similar aspect ratios or backgrounds. I will apply strategic pre-upload cropping so the model learns core hardware features rather than environmental noise. Having built dozens of edge-deployment classifiers, I will manually tune the epochs, batch size, and learning rate to prevent overfitting and guarantee >90% accuracy. I’ll provide the model export alongside a clear Loom walkthrough. Will your hold-out set include photos with hands covering the device edges? Let's review the dataset.

@mohamedg101

Hi, this is a great fit for a clean Teachable Machine prototype. The best approach is to build a simple but reliable image classifier with well-labeled iPad and iPhone examples, balanced training data, and smart augmentation so it performs well on unseen photos without becoming overly technical. I can train and tune the model to target 90%+ accuracy, validate it on a proper hold-out set, and deliver both the export and a very simple walkthrough your non-technical team can follow for demos or future retraining. The goal is not just a model that works—it’s a model people can actually understand, present, and reuse without needing a PhD to click “Train.”