Comparison CNNs for image classification task on Caltech101

File to be updated.

Evaluation of transfer learning and data augmentation in AlexNet, VGG-16 and ResNet for image classification on Caltech-101.
Experiments are carried out in PyTorch.

Dataset

Results

Training from scratch

Transfer Learning

Data Augmentation

Pretrained model comparison

🔗 More information about the experiments and discussion available in the report.

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References

[1] A. Krizhevsky et al. ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems. (2012)
[2] Fei-Fei, Li et al. “Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories.” 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004): 178-178.
[3] Simonyan, Karen and Andrew Zisserman. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” CoRR abs/1409.1556 (2015)
[4] He, Kaiming et al. “Deep Residual Learning for Image Recognition.” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016): 770-778.
[5] Dataset and split available via github repository - github