I have this attempt on classifying CIFAR10 images.
I’m curently training my SVM only on 1 of 5 sets, and decided on using polynomial kernel(degree=3,C=0.1 [changes have no effect],gamma=2 [changes have no effect]).
I’m getting ~20% accuracy and I want to know how can I improve.
I’m fitting images with X key-points to other images with the same number of key-points so the vectors will match.
Any tips on using the SVM better will be highly appreciated 🙂
# ============================================================================================================================================= # #Imports import cv2 import numpy as np # ============================================================================================================================================= # # ============================================================================================================================================= # #Globals: batch_dir = r'...' data_1 = batch_dir + '\' + r'data_batch_1' data_2 = batch_dir + '\' + r'data_batch_2' data_3 = batch_dir + '\' + r'data_batch_3' data_4 = batch_dir + '\' + r'data_batch_4' data_5 = batch_dir + '\' + r'data_batch_5' data_batch = [data_1, data_2, data_3, data_4, data_5] test = batch_dir + '\' + r'test_batch' # ============================================================================================================================================= # # ============================================================================================================================================= # # dict = {data : lables} # data = 10000*3072 numpy array of uint8 # Each row of the array stores a 32x32 colour image # The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue # The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image # lables = list of 10000 numbers in the range 0-9 # The number at index i indicates the label of the ith image in the array data # ============================================================================================================================================= # # The dataset contains another file, called batches.meta. It too contains a Python dictionary object. It has the following entries: # label_names = 10-element list which gives meaningful names to the numeric labels in the labels array described above. # For example, label_names[0] == "airplane", label_names[1] == "automobile", etc. # ============================================================================================================================================= # def unpickle(file, print_msg = False): if print_msg: print 'unpicking training set images' import cPickle with open(file, 'rb') as fo: dict = cPickle.load(fo) return dict # ============================================================================================================================================= # # input: array of 3072 elements # ============================================================================================================================================= # def img_2_RGB(im, print_msg = False): if print_msg: print 'reshaping image to RGB format (32*32*3)' mat = np.zeros((32,32,3), np.uint8) for i in range(32*32*3): mat[(i % (32*32)) // 32][i % 32][i // (32*32)] = im[i] return mat # ============================================================================================================================================= # # input: image list, heading (optional), delay [mili-seconds] (optional) # ============================================================================================================================================= # def show_img(i, heading = '', delay = 600): cv2.imshow(heading, i) cv2.waitKey(delay) cv2.destroyAllWindows() def my_main(num_of_images_to_check, num_of_des): print '#'*100 print 'using {} descriptors'.format(num_of_des) d = unpickle(data_1) tst = unpickle(test) images = d.values()[0][:num_of_images_to_check] labels = d.values()[1][:num_of_images_to_check] tst_images = tst.values()[0][:num_of_images_to_check] tst_labels = tst.values()[1][:num_of_images_to_check] sift = cv2.xfeatures2d.SIFT_create() relevant_sized_descriptors = [] relevant_labels = [] for im,lb in zip(images, labels): im = img_2_RGB(im) old_kp = sift.detect(im, None) kp,ds = sift.compute(im, old_kp) if ds is not None and len(ds) == num_of_des: relevant_sized_descriptors.append(ds.flatten()) relevant_labels.append(lb) X = np.array(relevant_sized_descriptors) y = np.array(relevant_labels) from sklearn import svm clf1 = svm.LinearSVC() clf2 = svm.SVR() clf3 = svm.SVC(kernel='rbf') clf4 = svm.SVC(C=0.01, kernel='poly', degree=3, gamma=2) clf5 = svm.SVC(C=0.01, kernel='poly', degree=4, gamma=2) clf6 = svm.SVC(C=0.05, kernel='poly', degree=3, gamma=2) clf7 = svm.SVC(C=0.05, kernel='poly', degree=4, gamma=2)#best so far clf8 = svm.SVC(C=0.01, kernel='poly', degree=5, gamma=2) clf9 = svm.SVC(C=0.1, kernel='poly', degree=4, gamma=2) clf10 = svm.SVC(C=0.1, kernel='poly', degree=5, gamma=2) clf11 = svm.SVC(C=0.3, kernel='poly', degree=4, gamma=2) clf12 = svm.SVC(C=0.5, kernel='poly', degree=4, gamma=2) clf13 = svm.SVC(C=1.0, kernel='poly', degree=4, gamma=2) clf14 = svm.SVC(C=2.0, kernel='poly', degree=4, gamma=2) clfs = [clf7] for clf in enumerate(clfs): print('fitting clf # {}'.format(clf[0]+1)) clf[1].fit(X,y) relevant_sized_descriptors = [] relevant_labels = [] for im,lb in zip(tst_images, tst_labels): im = img_2_RGB(im) old_kp = sift.detect(im, None) kp,ds = sift.compute(im, old_kp) #print old_kp, kp, ds if ds is not None and len(ds) == num_of_des: relevant_sized_descriptors.append(ds.flatten()) relevant_labels.append(lb) X = np.array(relevant_sized_descriptors) for clf in enumerate(clfs): print('clf # {}'.format(clf[0]+1), clf[1].score(X,relevant_labels)) #clean-up del relevant_sized_descriptors del relevant_labels del X del y del clf del d del tst del images del labels del tst_images del tst_labels del sift for i in range(20,30): my_main(10000, i) 
