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convet-study: A study of ConvNet architectures on small image datasets

This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).

Requirements

This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.

  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.

Getting the data

Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.

Training a model using the train.py script

This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like

%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO

This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument

%run train.py -l SAVE_FILE_NAME

This will resume training from the last checkpoint.

Training a model using your own script

All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.

Implemented models

Densenet

Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.

densenet no augmentation

densenet augmented

Batch-normalized Network in Network

Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].

NiNBN no augmentation

NiNBN augmented

Batch-normalized VGG-like network

Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.

VGGBN no augmentation

VGGBN augmented

References

  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.

About

No description, website, or topics provided.

Resources

Readme
MIT license
Activity

Stars

35 stars

Watchers

5 watching

Forks

19 forks
Report repository

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Used by

Contributors

Languages

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

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convet-study: A study of ConvNet architectures on small image datasets
This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).
Requirements
This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.
  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.
Getting the data
Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.
Training a model using the train.py script
This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like
%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO
This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument
%run train.py -l SAVE_FILE_NAME
This will resume training from the last checkpoint.
Training a model using your own script
All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.
Implemented models
Densenet
Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.
densenet no augmentation
densenet augmented
Batch-normalized Network in Network
Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].
NiNBN no augmentation
NiNBN augmented
Batch-normalized VGG-like network
Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.
VGGBN no augmentation
VGGBN augmented
References
  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
35 stars
Watchers
5 watching
Forks
19 forks
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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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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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convet-study: A study of ConvNet architectures on small image datasets
This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).
Requirements
This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.
  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.
Getting the data
Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.
Training a model using the train.py script
This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like
%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO
This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument
%run train.py -l SAVE_FILE_NAME
This will resume training from the last checkpoint.
Training a model using your own script
All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.
Implemented models
Densenet
Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.
densenet no augmentation
densenet augmented
Batch-normalized Network in Network
Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].
NiNBN no augmentation
NiNBN augmented
Batch-normalized VGG-like network
Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.
VGGBN no augmentation
VGGBN augmented
References
  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
35 stars
Watchers
5 watching
Forks
19 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
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convet-study: A study of ConvNet architectures on small image datasets
This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).
Requirements
This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.
  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.
Getting the data
Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.
Training a model using the train.py script
This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like
%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO
This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument
%run train.py -l SAVE_FILE_NAME
This will resume training from the last checkpoint.
Training a model using your own script
All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.
Implemented models
Densenet
Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.
densenet no augmentation
densenet augmented
Batch-normalized Network in Network
Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].
NiNBN no augmentation
NiNBN augmented
Batch-normalized VGG-like network
Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.
VGGBN no augmentation
VGGBN augmented
References
  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
35 stars
Watchers
5 watching
Forks
19 forks
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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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convet-study: A study of ConvNet architectures on small image datasets
This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).
Requirements
This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.
  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.
Getting the data
Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.
Training a model using the train.py script
This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like
%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO
This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument
%run train.py -l SAVE_FILE_NAME
This will resume training from the last checkpoint.
Training a model using your own script
All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.
Implemented models
Densenet
Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.
densenet no augmentation
densenet augmented
Batch-normalized Network in Network
Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].
NiNBN no augmentation
NiNBN augmented
Batch-normalized VGG-like network
Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.
VGGBN no augmentation
VGGBN augmented
References
  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
35 stars
Watchers
5 watching
Forks
19 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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convet-study: A study of ConvNet architectures on small image datasets
This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).
Requirements
This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.
  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.
Getting the data
Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.
Training a model using the train.py script
This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like
%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO
This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument
%run train.py -l SAVE_FILE_NAME
This will resume training from the last checkpoint.
Training a model using your own script
All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.
Implemented models
Densenet
Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.
densenet no augmentation
densenet augmented
Batch-normalized Network in Network
Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].
NiNBN no augmentation
NiNBN augmented
Batch-normalized VGG-like network
Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.
VGGBN no augmentation
VGGBN augmented
References
  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
35 stars
Watchers
5 watching
Forks
19 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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convet-study: A study of ConvNet architectures on small image datasets
This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).
Requirements
This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.
  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.
Getting the data
Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.
Training a model using the train.py script
This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like
%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO
This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument
%run train.py -l SAVE_FILE_NAME
This will resume training from the last checkpoint.
Training a model using your own script
All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.
Implemented models
Densenet
Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.
densenet no augmentation
densenet augmented
Batch-normalized Network in Network
Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].
NiNBN no augmentation
NiNBN augmented
Batch-normalized VGG-like network
Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.
VGGBN no augmentation
VGGBN augmented
References
  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.
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No description, website, or topics provided.
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35 stars
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5 watching
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19 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
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convet-study: A study of ConvNet architectures on small image datasets
This repository contains implementations of some architectures that I find interesting and get good results on small image datasets (mostly CIFAR-10).
Requirements
This code runs on Keras and has been tested with Tensorflow as backend. You should have a GPU to run this code on a reasonable amount of time.
  • Python 2.7.
  • Numpy.
  • Scipy.
  • Pyyaml.
  • Tensorflow.
  • Keras.
  • hdf5, h5py.
Getting the data
Download the code, run the get_dataset.sh script, it should download the datasets and place them on the appropriate folders.
Training a model using the train.py script
This script allows training (some) models using a command-line interface. You should pass at least a model loader and a dataset, all the other arguments are optional and have default values. Assuming you are in an ipython session, your call should be something like
%run train.py -d DATASET -m MODEL_LOADER -b BATCH_SIZE --l2 L2_REGULARIZATION -s SAVE_FILE_NAME --schedule LEARNING_RATE_SCHEDULE --lr LEARNING_RATE -e NUMBER_OF_EPOCHS -v VALID_RATIO
This script will automatically save the model at every epoch using keras model saving (SAVE_FILE_NAME.h5) along with a metadata file (SAVE_FILE_NAME.meta) that contains the arguments passed, training metrics and learning rate scheduling information. Should you want to resume training run the script with the -l[--load] argument
%run train.py -l SAVE_FILE_NAME
This will resume training from the last checkpoint.
Training a model using your own script
All the models are defined by a function in the models package. Calling it will return a Keras model that can be trained however you want. You can check the scripts directory for examples.
Implemented models
Densenet
Implementation of [1]. Currently only tested the smaller densenet: 40 layers total, 3 dense blocks with 12 layers with and growth rate of 12. The code should support the bigger models though. Accuracy of 93.58% without data augmentation (paper reports 93%) and 94.72% (paper reports 94.76%) with horizontal flips and crops.
densenet no augmentation
densenet augmented
Batch-normalized Network in Network
Architecture based on [2], modified to include batch normalization. Achieved 90.97% accuracy without data augmentation and 92.46% with flips. Uses learning rate schedule from [3].
NiNBN no augmentation
NiNBN augmented
Batch-normalized VGG-like network
Bases on [3], 91.68% accuracy without data augmentation and 92.92% with flips.
VGGBN no augmentation
VGGBN augmented
References
  • [1]: G. Huang, Z. Liu and K. Weinberger. Densely Connected Convolutional Networks. Arxiv. Github.
  • [2]: M. Lin, Q. Chen and S. Yan. Network in Network. Arxiv.
  • [3]: S. Zagoruyko. cifar.torch. Github.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
35 stars
Watchers
5 watching
Forks
19 forks
Report repository
Releases
Packages
Used by
Contributors
Languages