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Convolution Feature Map Visualizer 🏞️

A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.

Features

  • Upload an image in PNG, JPG, or JPEG format.

  • Adjust convolution parameters using an interactive sidebar:

    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.

  • Visualize each feature map in a grid layout.

  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.

Demo

demo

Installation

  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt

Usage

  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.

Convolution Parameters

  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.

Example Output

Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.

Built With

  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib

Author

Developed by Rohan Sardar Project completed on 2nd December 2025.

About

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Convolution Feature Map Visualizer 🏞️
A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.
Features
  • Upload an image in PNG, JPG, or JPEG format.
  • Adjust convolution parameters using an interactive sidebar:
    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.
  • Visualize each feature map in a grid layout.
  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.
Demo
demo
Installation
  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt
Usage
  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.
Convolution Parameters
  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.
Example Output
Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.
Built With
  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib
Author
Developed by Rohan Sardar
Project completed on 2nd December 2025.
About
Visualize Conv2d of PyTorch
Resources
Readme
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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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Convolution Feature Map Visualizer 🏞️
A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.
Features
  • Upload an image in PNG, JPG, or JPEG format.
  • Adjust convolution parameters using an interactive sidebar:
    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.
  • Visualize each feature map in a grid layout.
  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.
Demo
demo
Installation
  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt
Usage
  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.
Convolution Parameters
  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.
Example Output
Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.
Built With
  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib
Author
Developed by Rohan Sardar
Project completed on 2nd December 2025.
About
Visualize Conv2d of PyTorch
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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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Convolution Feature Map Visualizer 🏞️
A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.
Features
  • Upload an image in PNG, JPG, or JPEG format.
  • Adjust convolution parameters using an interactive sidebar:
    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.
  • Visualize each feature map in a grid layout.
  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.
Demo
demo
Installation
  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt
Usage
  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.
Convolution Parameters
  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.
Example Output
Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.
Built With
  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib
Author
Developed by Rohan Sardar
Project completed on 2nd December 2025.
About
Visualize Conv2d of PyTorch
Resources
Readme
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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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Convolution Feature Map Visualizer 🏞️
A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.
Features
  • Upload an image in PNG, JPG, or JPEG format.
  • Adjust convolution parameters using an interactive sidebar:
    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.
  • Visualize each feature map in a grid layout.
  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.
Demo
demo
Installation
  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt
Usage
  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.
Convolution Parameters
  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.
Example Output
Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.
Built With
  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib
Author
Developed by Rohan Sardar
Project completed on 2nd December 2025.
About
Visualize Conv2d of PyTorch
Resources
Readme
Activity
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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");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Convolution Feature Map Visualizer 🏞️
A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.
Features
  • Upload an image in PNG, JPG, or JPEG format.
  • Adjust convolution parameters using an interactive sidebar:
    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.
  • Visualize each feature map in a grid layout.
  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.
Demo
demo
Installation
  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt
Usage
  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.
Convolution Parameters
  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.
Example Output
Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.
Built With
  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib
Author
Developed by Rohan Sardar
Project completed on 2nd December 2025.
About
Visualize Conv2d of PyTorch
Resources
Readme
Activity
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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); }
})();
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var __m = "*";
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Convolution Feature Map Visualizer 🏞️
A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.
Features
  • Upload an image in PNG, JPG, or JPEG format.
  • Adjust convolution parameters using an interactive sidebar:
    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.
  • Visualize each feature map in a grid layout.
  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.
Demo
demo
Installation
  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt
Usage
  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.
Convolution Parameters
  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.
Example Output
Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.
Built With
  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib
Author
Developed by Rohan Sardar
Project completed on 2nd December 2025.
About
Visualize Conv2d of PyTorch
Resources
Readme
Activity
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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Convolution Feature Map Visualizer 🏞️
A Streamlit-based web app that allows you to visualize the feature maps generated by a convolution layer (Conv2d) in PyTorch. This tool is perfect for understanding how convolutional filters interact with images and exploring deep learning fundamentals.
Features
  • Upload an image in PNG, JPG, or JPEG format.
  • Adjust convolution parameters using an interactive sidebar:
    • Out Channels: Number of convolutional filters.
    • Kernel Size: Size of each convolutional filter.
    • Stride: Step size for sliding the kernel over the image.
    • Padding: Zero-padding added to the input image.
  • Visualize each feature map in a grid layout.
  • Developed entirely with PyTorch, Torchvision, PIL, Matplotlib, and Streamlit.
Demo
demo
Installation
  1. Clone the repository:
git clone https://github.com/RohanSardar/Conv2dVisualization
cd Conv2dVisualization
  1. Create and activate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
  1. Install dependencies:
pip install -r requirements.txt
Usage
  1. Run the Streamlit app:
streamlit run app.py
  1. Upload an image through the sidebar.
  2. Adjust convolution parameters.
  3. Click "Process Image" to visualize the feature maps.
Convolution Parameters
  • Out Channels: Controls how many different features the convolution layer extracts.
  • Kernel Size: Determines the size of the filter used to scan the image.
  • Stride: Determines how much the filter moves at each step.
  • Padding: Adds zeros around the image to control output size.
Example Output
Each filter produces a feature map showing activated patterns from the input image. This visualization helps understand how convolution layers detect edges, textures, and more complex patterns.
Built With
  • Python
  • Streamlit
  • PyTorch
  • Torchvision
  • PIL / Pillow
  • Matplotlib
Author
Developed by Rohan Sardar
Project completed on 2nd December 2025.
About
Visualize Conv2d of PyTorch
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