ECE 539 Project Report Birdspecies Recognition Using Convolutional
ECE 539 – Project Report Bird-species Recognition Using Convolutional Neural Network -- Instructor: Yu Hen Hu -- Presenter: Xucheng Wan
Problem Introduction Computer Vision → Recognition & Classification → Datasets
Caltech UCSD Bird 200 - 2011 (CUB-200) (Established for subordinate categorization study) 200 Bird-species * 60 images for each
Method and Progress I. Bird Recognition - Binary Classifier
I. Binary Classifier Data: Cifar(60 k)+ CUB-200(12 k) Validation: 20% Accuracy: 0. 951
II. Multi-Classifier # Classes increases → need more features → # Layers increase
II. Multi-Classifier # of Classes Training Accuracy Validation Accuracy 20 0. 87 0. 637 80 0. 68 0. 397 200 0. 42 0. 273 Data is so limited !!!! Only 60 images for each of 200 bird-species.
III. Revised Goog. Le. Net Modified some layers and activation functions
III. Revised Goog. Le. Net Dataset Subset 1 Class 1 ··· Class k Subset 10 Group Name Validation Loss Validation Accuracy Entire Dataset 0. 5718 0. 8202 Weighted Average 0. 4664 0. 8697
Conclusion & Evaluation Method Validation Accuracy Bird Recognition with Binary Classifier 0. 951 CNN with 5 Layers and Soft. Max 0. 2730 Revised Goog. Le. Net and Soft. Max 0. 4771 Revised Goog. Le. Net on 10 subsets 0. 7133 1. Multi-output activation > Binary classifier 2. More layers VS Overfitting 3. Data is the most important
Thank You ! Questions ?
- Slides: 11