FrImCla
FrImCla is an open-source framework for Image Classification using traditional and deep learning techniques. It supports a wide variety of deep learning and classical computer vision models.
Features
- Single-label and multi-label classification
- Several deep learning models
- Classical computer vision techniques
- Different performance measures
- Jupyter notebooks to use the framework
- Ability to test several models at the same time
- It is possible to easily add models and apply them into the framework to comprobe the performance over a dataset
- A web application automatically created to predict the image class
Requeriments and installation of the library
The library uses Python 3.6, which must be installed. frimcla can be installed using the following command:
pip install frimcla
To uninstall the library use pip uninstall frimcla.
Documentation
Documentation of the framework.
Examples
- How to use the framework.
- How to use the framework with majority voting (Ensemble technique).
- How to predict an image class.
- Example of use on the MIAS dataset.
- Example of use on the malaria dataset.
- Example of use on a dogs and cats dataset.
- Example of use on a multi-label dataset.
List of feature extractor models
| Model | Description | Parameters |
|---|---|---|
| VGG16 | Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition. It has been obtained by directly converting the Caffe model provived by the authors. | include_top(either True or False). |
| VGG19 | Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition. It has been obtained by directly converting the Caffe model provived by the authors. | include_top(either True or False). |
| Resnet | Residual neural networks utilizes skip connections or short-cuts to jump over some layers. In its limit as ResNets it will only skip over a single layer. With an additional weight matrix to learn the skip weights it is referred to as HighwayNets. With several parallel skips it is referred to as DenseNets. | include_top(either True or False). |
| Inception | The network used a CNN inspired by LeNet but implemented a novel element which is dubbed an inception module. It used batch normalization and image distortions. This module is based on several very small convolutions in order to drastically reduce the number of parameters. | include_top(either True or False). |
| Googlenet | This network used a new variant of convolutional neural network called “Inception” for classification, and for detection the R-CNN was used. Google’s team was able to train a much smaller neural network and obtained much better results compared to results obtained with convolutional neural networks in the previous year’s challenges. | No parameters. |
| Overfeat | It is a combination of CNN and another machine learning classifier ( LR, SVM, etc.). | output_layers(by default [-3]). |
| Xception | This architecture has 36 convolutional layers forming the feature extraction base of the network. The Xception architecture is a linear stack of depthwise separable convolution layers with residual connections. | include_top(either True or False). |
| Densenet | This is a stack of dense blocks followed by transition layers. Each block consists of a series of units. Each unit packs two convolutions, each preceded by Batch Normalization and ReLU activations. Besides, each unit outputs a fixed number of feature vectors. This parameter, described as the growth rate, controls how much new information the layers allow to pass through. | No parameters |
| LAB888 | Histogram of the images with the lab colour format with 8 bins | No parameters. |
| LAB444 | Histogram of the images with the lab colour format with 4 bins | bins(by default 4,4,4). |
| HSV888 | Histogram of the images with the hsv colour format with 8 bins | No parameters. |
| HSV444 | Histogram of the images with the hsv colour format with 4 bins | bins(by default 4,4,4). |
| Haralick | This model suggested the use of gray level co-occurrence matrices (GLCM). This method is based on the joint probability distributions of pairs of pixels. GLCM show how often each gray level occurs at a pixel located at a fixed geometric position relative to each other pixel, as a function of the gray level. An essential component is the definition of eight nearest-neighbor resolution cells that define different matrices for different angles (0°,45°,90°,135°) and distances between the horizontal neighboring pixels. | No parameters. |
| Hog | The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy. | No parameters. |
List of classifier models
List of classifiers for single-label models
| Technique | Description |
|---|---|
| Multilayer perceptron (MLP) | An MLP consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. |
| Support vector machine (SVM) | An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. |
| K-Nearest Neighbour(KNN) | The output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbour. |
| Logistic Regression (LR) | A logistic model is one where the log-odds of the outputs are a linear function of the input. The two possible output values are often labelled as "0" and "1", which represent outcomes such as pass/fail, win/lose or alive/dead. The logistic model generalizes easily to multiple inputs, where the log-odds are linear in all the inputs; and, with more modification, to more outputs. |
| Random Forest (RF) | These models are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. |
| Gradient Boost (GB) | It is a technique for regression and classification techniques. Gradient Boost produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model like other boosting methods do and it generalizes them by allowing optimization of an arbitrary defferentiable loss function. |
List of classifiers for multi-label models
| Technique | Description |
|---|---|
| Binary relevance | It treats each label as a separate single-class classification problem. It can use any of the classifiers for single-label models |
| ClassifierChain | It treats each label as a part of a conditioned chain of single-class classification problems. It can use any of the classifiers for single-label models |
| LabelPowerset | It treats each label combination as a separate class with one multi-class classification problem. It can use any of the classifiers for single-label models |
| MlKnn | A multi-label adapted kNN classifier with bayesian prior corrections. |
| MLTSVM | Twin multi-Label Support Vector Machines |
