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Graduation project based on multi-label learning
- 1. 我们毕业啦其实是答辩的标题地方 multi-label learning library & multi-label scene classification Lxinyuelxy JiangNan University
- 2. CONTANTS • Introduction • mllearn library • scene classification • summary
- 3. IntroductionIntroduction Traditional classification f1( ) = "cat" f3( ) = "{sunset, sea, boat}" f2( ) = "0" • Binary classification • multi-class classification • multi-label classification
- 4. Introduction Sunset Trees Mounts Sea Desert Grassland Yes No No Yes No No What is multi-label classification ?
- 5. Introduction The data format in multi-label classification X Y X(1) 0 X(2) 1 X(3) 0 X(4) 1 X(5) 0 X ? X Y1 Y2 Y3 Y4 Y5 X(1) 0 1 1 1 0 X(2) 1 0 1 0 0 X(3) 0 0 0 1 1 X(4) 0 1 1 0 0 X(5) 1 0 0 0 1 X ? ? ? ? ? Sing-lable Multi-label
- 6. •Problem Transformation •Algorithm Adaptation Introduction Strategies for solving a multi-Label classification Transformed Dataset multi-label Dataset tradition algs adapted algs tradition algs multi-label Dataset
- 7. Introduction Metrics of multi-Label classification • Subset Accuracy • Hamming Loss predict real {sunset, sea} {sunest, sea, trees} × {trees, sunset} {trees, sunset} √ Label space predict real
- 9. mllearn library Implemented algorithms • Multi-Label k-Nearest Neighbor • Ranking Support Vector Machine • Multi-Lanel Decision Tree • Collective Multi-Label Classifier • Subset Accuracy • Hamming Loss • Accuracyexam • Precision • Recall • Fβ exam mllearn • Bianary Relevance • Classifier Chains • Calibrated Lable Ranking • Random k-Labelsets metrics algs problem transform algs adaptation https://github.com/Lxinyuelxy/multi-label-learn
- 10. Introduction The data format for multi-label classificationmllearn library >>> type(X) <type 'numpy.ndarray'> >>> X_train.shape (n_samples, n_features) >>> type(y) <type 'numpy.ndarray'> >>> y.shape (n_samples, n_labels) • Data Format X1 1, X1 2, ......, X1 n_features X2 1, X2 2, ......, X2 n_features X3 1, X3 2, ......, X3 n_features Xm 1, Xm 2, ......, Xm n_features ...... ...... ...... ...... 1, 0, 0, 1, 0 0, 1, 1, 1, 0 1, 0, 0, 0, 1 1, 0, 1, 1, 0, ...... ...... ......
- 11. Introduction Installation & Example Usagemllearn library from mllearn.problem_transform import BinaryRelevance #from sklearn.svm import SVC #classif = BinaryRelevance(SVC(kernel='linear')) classif = BinaryRelevance() classif.fit(X_train, y_train) y_pred = classif.predict(X_test) from mllearn.metrics import subset_acc acc = subset_acc(y_test, y_pred) pip install mllearn • Installation • Example Usage
- 12. Introduction The Datasets for testmllearn library
- 14. The datasetscene classify Label Set #Images Label Set #Images Label Set #Images desert 340 desert+ sunset 21 sunset+trees 28 mounts 268 desert+trees 20 desert+mounts +sunset 1 sea 341 mounts+sea 38 desert+ sunset+trees 3 sunset 216 mountains+ sunset 19 mounts+sea+ trees 6 trees 378 mountains+ trees 106 mounts+ sunset+trees 1 desert+ mounts 19 sea+sunset 172 sea+sunset+ trees 4 desert+sea 5 sea+trees 14 Total 2000 http://lamda.nju.edu.cn/(X(1)S(10cedth11swjtqtcqjmwei1g))/Default.aspx?Page=dat a_MIMLimage&NS=&AspxAutoDetectCookieSupport=1
- 15. Introduction How to choose a feature extractor ?scene classify Algorithms Binary Relevance Classifier Cahins Calibrated Label Ranking Random k- Labelsets MLKNN Implemented Hamming Loss 0.113573 0.115803 0.107581 0.097826 0.097408 0.19650 Subset Accuracy 0.496656 0.6112 0.492475 0.610368 0.568562 0.28500
- 16. Introduction How to choose a feature extractor ?scene classify mllearn library Traditional feature extraction method [X1, X2, ..., Xd] feature extraction daisy hog Hamming loss 0.251 0.18950 Subset Accuracy 0.3275 0.44
- 17. Solution 1: Data Augmentationscene classify
- 18. Solution 1: CNN binary classifyscene classify trees sunset sea mounts desert Hamming Loss Subset Accuracy CNN 0.11975 0.575 https://github.com/Lxinyuelxy/multi-label-scene-classification
- 19. Model Size Top-1 Accuracy Parameters Feature Shape VGG16 528 MB 0.715 138,357,54 4 (1, 7, 7, 512) VGG19 549 MB 0.727 143,667,24 0 (1, 7, 7, 512) Inception V3 92 MB 0.788 23,851,784 (1, 5, 5, 2048) ResNet50 99 MB 0.759 25,636,712 (1, 1, 1, 2048) Inception ResNetV2 215 MB 0.804 55,873,736 (1, 8, 8, 1536) Solution 2: RestNet50 + mllearnscene classify mllearn library ResNet50 model [X1, X2, ..., X2048] https://github.com/Lxinyuelxy/multi-label-scene-classification
- 21. multi-label scene classify appscene classify
- 23. The process of multi-label scene classificationsummery feature extractor data multi-label algorithm model unseen instance show output upload image train predict application
- 25. THANKS