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Image Classification Pipeline Based on K-nearest Neighbor or The Svm/Softmax Classifier (Lab Report Sample)


In this assignment you will practice putting together a simple image classification pipeline based on the k-Nearest Neighbor or the SVM/Softmax classifier. The goals of this assignment are as follows:
Understand the basic Image Classification pipeline and the data-driven approach (train/predict stages).
Understand the train/val/test splits and the use of validation data for hyperparameter tuning.
Develop proficiency in writing efficient vectorized code with numpy.
Implement and apply a k-Nearest Neighbor (kNN) classifier.
Implement and apply a Multiclass Support Vector Machine (SVM) classifier.
Implement and apply a Softmax classifier.
Implement and apply a Two layer neural network classifier.
Understand the differences and tradeoffs between these classifiers.
Get a basic understanding of performance improvements from using higher-level representations as opposed to raw pixels, e.g. color histograms, Histogram of Gradient (HOG) features, etc.


Assignment 11
# from
import matplotlib
from nuscenes.nuscenes import NuScenes
nusc = NuScenes(version='v1.0-mini',
dataroot='/data/users/chenjiaqi/dataset/nuScenes_mini', verbose=True)
# NuScenes()方法

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