CS231n-lecture2-Image Classification pipeline 课堂笔记
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相关资源
| Event Type | Date | Description | Course Materials |
| Lecture 2 | Thursday April 6 |
Image Classification The data-driven approach K-nearest neighbor Linear classification I |
[slides] [python/numpy tutorial] [image classification notes] [linear classification notes] |
作业
It is due January 20 (i.e. in two weeks). Handed in through CourseWork It includes: - Write/train/evaluate a kNN classifier - Write/train/evaluate a Linear Classifier (SVM and Softmax) - Write/train/evaluate a 2-layer Neural Network (backpropagation!) - Requires writing numpy/Python code
Python Numpy
PPT
图像识别
语义鸿沟问题semantic gap
Images are represented as 3D arrays of numbers, with integers between [0, 255].
挑战：（1）Viewpoint Variation 相机需要调整，使其具有鲁棒性。
（2）光线
（3）Deformation变形，姿势
（3）Occlusion遮蔽问题，只能看清所判别种类的一部分，e.g. 10%
（4）background clutter 背景杂斑
（5）Intraclassvariation 同类演变
Data-driven approach:
1. Collect a dataset of images and labels
2. Use Machine Learning to train an image classifier
3. Evaluate the classifier on a withheld set of test images
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相关资源
| Event Type | Date | Description | Course Materials |
| Lecture 2 | Thursday April 6 |
Image Classification The data-driven approach K-nearest neighbor Linear classification I |
[slides] [python/numpy tutorial] [image classification notes] [linear classification notes] |
作业
It is due January 20 (i.e. in two weeks). Handed in through CourseWork It includes: - Write/train/evaluate a kNN classifier - Write/train/evaluate a Linear Classifier (SVM and Softmax) - Write/train/evaluate a 2-layer Neural Network (backpropagation!) - Requires writing numpy/Python code
Python Numpy
PPT
图像识别
语义鸿沟问题semantic gap
Images are represented as 3D arrays of numbers, with integers between [0, 255].
挑战：（1）Viewpoint Variation 相机需要调整，使其具有鲁棒性。
（2）光线
（3）Deformation变形，姿势
（3）Occlusion遮蔽问题，只能看清所判别种类的一部分，e.g. 10%
（4）background clutter 背景杂斑
（5）Intraclassvariation 同类演变
Data-driven approach:
1. Collect a dataset of images and labels
2. Use Machine Learning to train an image classifier
3. Evaluate the classifier on a withheld set of test images