EfficientNet ViT Potato Disease Model

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@nrajuu07

With my extensive experience and strong track record in deep learning and computer vision, I am confident I can deliver all the requirements for your EfficientNet ViT Potato Disease Model. I have successfully built numerous production-ready models using EfficientNet, ViT, CNNs, and Transformers for various image classification tasks, including medical imaging with similar formats as yours. My skills with TensorFlow/Keras or PyTorch will ensure that the reproducible training notebook or script, as well as the inference script or REST API, are well-implemented, efficient, maintainable and user-friendly. Moreover, I understand that benchmarking is significant to validate model performance. Hence, for your project, I will utilize metrics like accuracy, precision recall and even offer an interpretable confusion matrix for a comprehensive evaluation. With my technical skillset firmly grounded in AI on real-world datasets- including agricultural ones-and a commitment to quality, you can trust me to deliver an unmatched solution tailored to your unique project requirements. Let's get started turning your potato leaves into rich data for disease detection!

@adriank06

Hi! ? Your dataset setup is perfect for a hybrid architecture, and I’ve built similar CNN‑plus‑Transformer models before for plant‑leaf and crop‑disease detection. I can take your labeled JPG/PNG set, clean/augment it, and train a production‑ready pipeline where EfficientNet handles feature extraction and a ViT‑style attention head refines the classification for your three classes. You’ll get: • A fully trained hybrid model (saved weights + ONNX/TensorFlow export) • A clean training script/notebook you can rerun anytime • A simple inference script or REST API that returns class + confidence • Short documentation so you can retrain/update the model yourself If you want, send a small sample of your dataset structure and I’ll outline the exact training pipeline before we start.

@PyMLExpert1

Drawing from my rich experience as a Python and Machine Learning Expert, I am confident in my ability to deliver an exemplary implementation. Having successfully designed several hybrid architectures blending CNN backbones with transformer heads, including on agricultural datasets, I am uniquely qualified to handle your project. As evidenced by my portfolio, I have excelled at leveraging computer vision and deep learning techniques to create accurate models. In terms of the pipeline you require, I'm well-versed in data cleaning and augmentation - imperative for the training process. I am proficient in TensorFlow/Keras and PyTorch; therefore, developing a clean and concise notebook or script for your future usage won't pose any challenge. Similarly, providing you with ONNX or TensorFlow format weights coupled with a functional inference script/REST API will be done seamlessly. Lastly, but equally important is documentation. I understand the significance of transparency and reproducibility in any ML project. Therefore, you can expect detailed documentation that will not only help you understand the process but also retrain or update the model effectively in subsequent stages. Partnering with me ensures your project will receive expert-level supervision that guarantees solid results and peace of mind

@InjectAI

I’ve worked on similar vision projects where CNN backbones are combined with attention-based models, especially for agricultural and fine-grained classification tasks, so I understand the challenges like class overlap and overfitting. My approach:- start with properly cleaning and structuring your dataset, followed by strong augmentation (rotation, brightness changes, noise, blur, and class balancing) to improve generalization. For the model, I’ll design a hybrid EfficientNet + Vision Transformer architecture, where EfficientNet handles deep feature extraction and ViT layers capture global relationships in the leaf patterns. This combination works well for disease classification where both texture and spatial context matter. After that, I’ll train and fine-tune the model specifically for your three classes: Blight, Early Blight, and Leaf Spot, while carefully monitoring validation performance to avoid overfitting and tuning learning rate and regularization where needed. Evaluation:- I’ll provide accuracy, precision, recall, F1-score, and a confusion matrix so you can clearly see class-wise performance. Final delivery :- It include trained weights (ONNX or TensorFlow format), a clean and reproducible training script/notebook, and an inference script or simple API that takes an image and returns the predicted class with confidence score. I’ll also add short documentation so you can retrain or update the model easily.

@dominicc54

Hello Mate!Greetings , Good evening! I am an expert mobile coder with skills including Deep Learning, Python, Image Processing, REST API, Machine Learning (ML), Computer Vision, Data Augmentation and Keras. Please contact me to discuss more about this project. For more details Chat with us

@salmaabdelhalim0

Hello, I understand that you already have a well-labeled dataset of potato leaves and you are looking to build a production-ready hybrid model combining EfficientNet for feature extraction with Vision Transformer attention to accurately classify Blight, Early blight, and Leaf spot. Based on my experience in deep learning and computer vision, I can handle the full pipeline from data preprocessing and augmentation to model design, training, and evaluation. I have worked on image classification and deep learning projects, and I can build and fine-tune a hybrid EfficientNet + ViT architecture, ensuring strong performance across your three classes. I will also evaluate the model using clear metrics like accuracy, precision, recall, and confusion matrix, and optimize it for better generalization. I can provide a clean training notebook or script, export the trained model in ONNX or TensorFlow format, and build a simple inference script or REST API that takes an image and returns predictions with confidence. I also focus on writing clear documentation so you can easily retrain or extend the model later. I’m ready to start immediately and would be happy to discuss your dataset and requirements. Best regards, Salma

@darshana5d

Hello, I am very interested in your potato leaf disease detection project. I have hands-on experience in Machine Learning and Computer Vision, including building models using Python, TensorFlow/Keras, and working with image datasets. I have also worked on projects involving data preprocessing, feature engineering, and model evaluation. For your requirement, I can: • Clean and augment your dataset for better generalization • Build a hybrid model combining EfficientNet for feature extraction and Vision Transformer (ViT) for attention-based learning • Fine-tune the model specifically for Blight, Early Blight, and Leaf Spot classification • Evaluate performance using accuracy, precision, recall, and confusion matrix • Provide a trained model (TensorFlow/PyTorch + ONNX export) • Deliver a complete training notebook for reproducibility • Develop an inference script or REST API for real-time predictions I focus on writing clean, reusable code and delivering production-ready solutions. I can start immediately and ensure timely delivery. Looking forward to discussing your project. Thank you, Darshana

@find1234

I have extensive experience building hybrid CNN+Transformer models for agricultural applications. My approach: **Architecture Design:** - EfficientNet-B0/B1 as backbone (efficient for edge deployment) - ViT-style attention blocks for global context - Custom classification head for 3 disease classes **Pipeline:** 1. Data augmentation (rotation, flip, color jitter, MixUp) 2. Transfer learning from ImageNet weights 3. Fine-tuning with class-balanced sampling 4. Evaluation with accuracy, precision, recall, F1, confusion matrix **Deliverables:** - TensorFlow/Keras training notebook - Saved model weights (.h5) - ONNX export for deployment - REST API (FastAPI) for inference - Documentation with retraining guide **Why me:** - 3+ years DL experience with similar agricultural projects - Published work on plant disease detection - Fast delivery with clean, documented code Ready to start immediately. Can deliver in 5 days with milestone updates.

@markdennis19

I can develop a production-ready hybrid model for detecting potato leaf diseases by combining EfficientNet’s feature extraction with Vision Transformer (ViT) attention mechanisms. The pipeline will include data preprocessing, augmentation, model design, training, and fine-tuning for Blight, Early Blight, and Leaf Spot detection. Key Deliverables: A fully trained hybrid model (EfficientNet + ViT) with saved weights in ONNX or TensorFlow format A reproducible training notebook or script using Python, TensorFlow/Keras, or PyTorch An inference script or minimal REST API for predicting new leaf images with confidence Clear documentation for retraining or updating the model I have experience blending CNN backbones with transformer heads, particularly for agricultural datasets, and will ensure the model is optimized and benchmarked with clear metrics (accuracy, precision, recall, confusion matrix). Let’s get started on building this robust solution!

@hammadfarooq1122

Hi, Your project is right in my wheelhouse — I've built CNN-Transformer hybrid architectures before, including on agricultural image datasets, so I understand exactly what this pipeline demands. My plan: I'll use EfficientNet-B4 as the CNN backbone for local texture features (lesion edges, colour patterns), feeding its feature maps into a lightweight ViT attention head to capture global leaf context. The fusion layer combines both representations before the 3-class softmax output (Blight, Early Blight, Leaf Spot). Pipeline: Data cleaning + augmentation (flips, rotations, colour jitter, mixup) to maximise your existing labeled set Transfer learning from ImageNet weights + fine-tuning on your dataset Full benchmarking: accuracy, precision, recall, F1, confusion matrix Export to ONNX + TensorFlow SavedModel format Deliverables (all within 7 days): Trained model with saved weights (ONNX + TF) Clean, reproducible Jupyter notebook (PyTorch) FastAPI inference endpoint: upload image → get class + confidence score Concise retraining guide One question: approximately how many images per class do you have? This helps me decide augmentation intensity upfront. Ready to begin immediately.

@radarabadi

"I am an AI Engineer with direct experience in building Hybrid CNN-Transformer architectures. I have recently developed a similar hybrid model fusing DenseNet with Vision Transformers (ViT) for complex feature selection, making me uniquely qualified for your potato leaf disease detection project. I can handle the full pipeline: from advanced data augmentation to delivering a production-ready model in ONNX format and a clean REST API for inference. I am ready to share my architectural approach and metrics from previous hybrid builds to demonstrate my expertise."

@lightninglabs

I specialize in deep learning for computer vision and can build this EfficientNet + ViT hybrid model for potato disease detection. My approach: - Data pipeline: augmentation (flip, rotation, color jitter, mixup) to maximize your JPG/PNG dataset - Architecture: EfficientNet-B4 backbone fused with ViT attention heads for multi-scale feature extraction - Training: transfer learning + fine-tuning for your 3 classes (Blight, Early blight, Leaf spot) with class-weighted loss - Evaluation: full metrics — accuracy, precision, recall, F1, confusion matrix, ROC curves - Deliverables: saved weights + ONNX/TF export, reproducible training notebook (TensorFlow/Keras), inference script I've built similar hybrid CNN-Transformer models for agricultural and medical imaging. I can start immediately and deliver within your timeframe. Happy to discuss architecture choices before starting.

@ayatnoor2466

Asma Ayyaz is here I'm able to do your work according to your requirements I'm already doing this type of work if you want then chk my work and make meeting.

@tarekhemdan

I'm a professor at the Department of Computer Science in Faculty of Science, Minia University, where I'm specializes in research areas such as Artificial Intelligence, Computer and Society, and Data Mining. With vast expertise in Pattern Recognition, Classification, Machine Learning, Image Processing, Computer Vision, Feature Extraction, Signal, Image and Video Processing, Feature Selection, Pattern Classification, Object Recognition, Image Segmentation, Data Mining and Knowledge Discovery, Image Data Analysis, Video Processing, Digital Image Processing, Image Analysis, Face Recognition, Face Detection, Segmentation, Image Recognition, Knowledge Discovery, Semantic Web, Web Mining, Information Technology, Information Extraction, Websites, Web of Data, Advanced Machine Learning, Supervised Learning, Machine Vision, and other related fields, I'm currently engaged in a project that utilizes Machine Learning algorithms to predict optimal drug combinations.

@Ahmed5168

Hi I have experience in Deep Learning and Computer Vision using TensorFlow/Keras, and I can build this EfficientNet + ViT hybrid model for you. I'll handle the full pipeline: data augmentation, model training, evaluation (accuracy, precision, recall, confusion matrix), and deliver the trained model + a simple REST API for inference. One question: how many images do you have per class? This helps me plan the augmentation. Ready to start immediately. Best regards

@LianSantana

Hello, Your project is a strong fit for my experience in deep learning and computer vision. I have worked with image classification pipelines and can build a reliable hybrid model combining EfficientNet and Vision Transformer to accurately detect. Since your dataset is already labeled, I will focus on maximizing performance and robustness. My approach includes data validation and targeted augmentation to improve generalization, followed by designing a hybrid architecture where EfficientNet acts as a feature extractor and ViT refines attention for better classification of Blight, Early Blight, and Leaf Spot. I will train and fine-tune the model, optimizing hyperparameters and providing clear evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix. My focus is to deliver a production-ready, well-structured solution that you can reuse and scale. I am available to start immediately and can adapt to your preferred framework. Best regards, Lian Santana