Remove 2015 Remove Computer Science Remove Support Vector Machines
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Faster R-CNNs

PyImageSearch

2015 ), SSD ( Fei-Fei et al., 2015 ; Redmon and Farhad, 2016 ), and others. Step #3: Use transfer learning, specifically feature extraction, to compute features for each proposal (effectively an ROI) using the pre-trained CNN. Step #4: Classify each proposal using the extracted features with a Support Vector Machine (SVM).

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Best Machine Learning Datasets

Flipboard

In their debut paper, they used a support-vector machine and only messed up 0.8% CelebA is the ultimate playground for anyone working in computer vision. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or requires a degree in computer science?