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Assignment 3 Image Classification using RF and SVM

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Introduction to Computer Vision (ECSE 415)
Assignment 3

Please submit your assignment solutions electronically via the myCourses
assignment dropbox. Attempt all parts of this assignment. The assignment will
be graded out of total of 33 points.
Submission Instructions
1. Prepare and submit two separate Google Colab notebooks for the two
questions.
2. Comment your code appropriately.
3. Do not submit input/output images. Assume image folders are kept in a
same directory as the codes.
4. Make sure that the submitted code is running without error. Add a
README file if required.
5. Answers to reasoning questions should be comprehensive but concise.
6. Submissions that do not follow the format will be penalized 10%.
Note that you can use any of the OpenCV, sklearn, skimage and Pytorch
functions shown during the tutorial sessions.
1 Image Classification using RF and SVM
For this task, you are given a dataset of flower images1
. The dataset contains
images of 9 types of flowers. You can read the images and the corresponding
labels as follows.
1The dataset is derived from the 102-Category Flower dataset[1].
1
train images = np.load(‘flower subset.npz’)[‘train images’]
train labels = np.load(‘flower subset.npz’)[‘train labels’]
test images = np.load(‘flower subset.npz’)[‘test images’]
test labels = np.load(‘flower subset.npz’)[‘test labels’]
The arrays train images and test images are stacks of 1556 and 90 grayscale images of size 128×128, respectively.
• Resize the train/test images to 64 × 64 and compute HoG features using
cells of 8×8 pixels, blocks of 4×4 cells and 4 bins. This should yeild a
feature vector of size 1600 per image. (3 points)
(Suggestion: Make a function which takes list of images as arguments and
delivers list of HoG features as output. The same function can be used for
train and test set.)
• Fit a non-linear SVM classifier (use RBF kernel with gamma=‘auto’ and
C=1) on the features and the class labels of the training images. (1
points)
• Predict labels of the test images by feeding the test features to the trained
classifier and calculate classification accuracy. (2 points)
• Tune values of hyperparameters ‘gamma’ and ‘C’ to achieve test accuracy
greater than 50%. (2 points)
• Fit a Random Forest(RF) classifier (set n estimators=10, max depth=5
and criterion=‘entropy’) on the features and the class labels of the training
images. (1 points)
• Predict labels of the test images by feeding the test features to the trained
classifier and calculate classification accuracy. (2 points)
• Tune values of hyperparameters ‘n estimators’ and ‘max depth’ to achieve
test accuracy greater than 50%. (2 points)
• Compare results of SVM and RF classifiers. Which one provides better
results? Experiment training both classifiers with a range of random states
(try different random values for the argument ‘random state’) and measure
classification accuracy of the test set. Which classifier is more stable or
robust to the change in random state? (3 points)
2 Image Classification with Convolution Neural
Network (CNN).
In this part, you will classify MNIST digits [2] into 10 categories using a CNN.
You may chose to run the code on GPU.
2
1. Use Pytorch class torchvision.datasets.MNIST to (down)load the dataset.
Use batch size of 32. (3 points)
2. Implement a CNN with the layers mentioned below. (5 points)
• A convolution layer with 32 kernels of size 3×3.
• A ReLU activation.
• A convolution layer with 64 kernels of size 3×3.
• A ReLU activation.
• A maxpool layer with kernels of size 2×2.
• A convolution layer with 64 kernels of size 3×3.
• A ReLU activation.
• A convolution layer with 64 kernels of size 3×3.
• A ReLU activation.
• A flattening layer. (This layer resizes 2D feature map to a feature
vector. The length of this feature vector should be 4096.)
• A Linear layer with output size of 10.
(Suggestion: you can start with the code from Tutorial 6 and adapt it for
the current problem.)
3. Create an instance of SGD optimizer with learning rate of 0.001. Use
the default setting for rest of the hyperparameters. Create an instance of
categorical cross entropy criterion. (1 point)
4. Train the CNN for 10 epochs. (5 points)
5. Predicts labels of the test images using the above trained CNN. Measure
and display classification accuracy. (3 points)
References
[1] Nilsback, Maria-Elena, and Andrew Zisserman. ”Automated flower classification over a large number of classes.” 2008 Sixth Indian Conference on
Computer Vision, Graphics & Image Processing. IEEE, 2008.
[2] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. ”Gradient-based learning
applied to document recognition.” Proceedings of the IEEE, 86(11):2278-
2324, November 1998.
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