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DishVision: Computer Vision Multi-Class Image Classifier

This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.

Key Features

  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.

Repository Contents

  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.

Getting Started

  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks: Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks: Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

About

Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.

huggingface.co/spaces/IsraelAzoulay/DishVision

Topics

artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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DishVision: Computer Vision Multi-Class Image Classifier
This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.
Key Features
  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.
Repository Contents
  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.
Getting Started
  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks:
Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks:
Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
About
Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.
huggingface.co/spaces/IsraelAzoulay/DishVision
Topics
artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DishVision: Computer Vision Multi-Class Image Classifier
This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.
Key Features
  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.
Repository Contents
  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.
Getting Started
  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks:
Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks:
Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
About
Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.
huggingface.co/spaces/IsraelAzoulay/DishVision
Topics
artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DishVision: Computer Vision Multi-Class Image Classifier
This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.
Key Features
  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.
Repository Contents
  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.
Getting Started
  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks:
Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks:
Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
About
Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.
huggingface.co/spaces/IsraelAzoulay/DishVision
Topics
artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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DishVision: Computer Vision Multi-Class Image Classifier
This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.
Key Features
  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.
Repository Contents
  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.
Getting Started
  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks:
Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks:
Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
About
Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.
huggingface.co/spaces/IsraelAzoulay/DishVision
Topics
artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DishVision: Computer Vision Multi-Class Image Classifier
This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.
Key Features
  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.
Repository Contents
  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.
Getting Started
  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks:
Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks:
Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
About
Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.
huggingface.co/spaces/IsraelAzoulay/DishVision
Topics
artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning
Resources
Readme
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Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DishVision: Computer Vision Multi-Class Image Classifier
This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.
Key Features
  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.
Repository Contents
  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.
Getting Started
  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks:
Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks:
Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
About
Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.
huggingface.co/spaces/IsraelAzoulay/DishVision
Topics
artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning
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DishVision: Computer Vision Multi-Class Image Classifier
This repository houses an advanced project focused on classifying images of three distinct types of food using cutting-edge deep learning and computer vision techniques. The project leverages transfer learning with PyTorch to achieve an impressive accuracy of 96+% on the test set. Additionally, it includes a detailed replication of the Vision Transformer (ViT) architecture from a prominent machine learning research paper, showcasing both the potential and challenges of this advanced approach. The implementation is executed on Google Colab for efficient computation and easy accessibility. The optimal model has been deployed on Hugging Face as an interactive app and can be accessed here.
Key Features
  • Data Source: Preprocessed food image datasets, prepared in the 'Structuring_the_Food101_Dataset' notebook.
  • Deep Learning Framework: PyTorch
  • Model Type: A diverse range of models including Transfer Learning Feature Extraction, Transfer Learning with Data Augmentation, TinyVGG, Vision Transformer (ViT) with Machine Learning Research Paper replication, and more.
  • Objective: Classifying images into three distinct food categories.
  • Accuracy: Achieves an exceptional 96+% accuracy on the test set.
  • Development Environment: Google Colab for development and execution.
  • Deployment: The optimal model is deployed on Hugging Face as an interactive app.
Repository Contents
  • Notebooks:
    • Structuring_the_Food101_Dataset: Notebook dedicated to preparing and structuring the Food-101 dataset sourced from 'torchvision.datasets'.
    • Computer_Vision_Multi_Class_Image_Classifier_Project: Comprehensive notebook detailing data loading and preprocessing, model training, evaluation, prediction, saving, deployment, and more.
  • Scripts:
    • helper_functions.py: Contains essential utility functions required for the project, located in the 'helpers' folder.
  • Data: Includes preprocessed datasets and a custom image for prediction purposes, located in the 'data' folder.
Getting Started
  1. Clone the repository:
!git clone https://github.com/IsraelAzoulay/multi-class-image-classifier-computer-vision.git
  1. Open the provided Google Colab notebooks:
Navigate to the 'notebooks' folder and open the desired notebook in Google Colab.
  2. Run the Notebooks:
Follow the instructions in the notebooks to download the datasets, preprocess the data, train, evaluate, predict, save and deploy the models.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
About
Classifying images of three different types of food using deep learning and computer vision techniques with PyTorch. Achieved 96+% accuracy on the test set. Deployed on Hugging Face as an interactive app.
huggingface.co/spaces/IsraelAzoulay/DishVision
Topics
artificial-intelligencecomputer-visiondata-sciencedeep-learningfeature-extractionmachine-learningmachine-learning-algorithmsmachine-learning-papersneural-networkpythonpytorchtransfer-learning
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages