- Use the CIFAR’s dataloader
- Read training images and labels in the numpy arrays.
- Train them using Nearest Neighborhood Classifier.
- Read the Test images and predict the test images with your nearest neighborhood classifier.
- Calculate the confusion matrix, accuracy, and F1 score
- Modify code for kNN classifier. Change K =3, and compare the performance
tahmid1999 / basic-image-classification-pipeline Goto Github PK
View Code? Open in Web Editor NEW1) Understand the basic Image Classification pipeline and the data - driven appr oach (train/predict stages) 2) Develop proficiency in writing efficient vectorized code with numpy 3) Implement and apply a k - Nearest Neighbor ( kNN ) classifier
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