논문 ID: net/5955809
Performing Image Classification for 10 Different Monkey Species using CNN
원문 초록
the main aim of this project is to achieve fine grain image classification by applying a suitable machine learning architecture to the set of images present in the dataset. The chosen dataset is taken as a part of the Kaggle competition and is selected from Wikipedia's monkey cladogram and this dataset contains 10 different species of monkeys which are to be classified with the help of a machine learning architecture augmented by Image processing. After having brief exposure and using several architectures to classify this dataset, the Convolutional Neural network was found to be the best fit.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Are These Birds Similar: Learning Branched Networks for Fine-grained Representations
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image Classification
Cross-Part Learning for Fine-Grained Image Classification
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.