We provide fine-tuned models for CUB200-2011 birds (AlexNet + VGG19), Oxford flowers 102 (AlexNet + VGG19), Oxford IIIT PETS (AlexNet + VGG19), and NABirds dataset (GoogLeNet). We also provide our AlexNet model which was trained on ImageNet with the Stanford dogs test data excluded.

No bounding box or part annotations were used for fine-tuning. Part-based object proposal filtering and two-step fine-tuning was used as described in the corresponding paper

<br/>@inproceedings{Simon15:NAC,<br/> author = {Marcel Simon and Erik Rodner},<br/> booktitle = {International Conference on Computer Vision (ICCV)},<br/> title = {Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks},<br/> year = {2015},<br/>}<br/>

[[Models](https://drive.google.com/file/d/0B6VgjAr4t_oTQXN2Y3VYaEMwVDA/view?usp=sharing)] [[Paper](http://arxiv.org/abs/1504.08289)] [[Github repo](https://github.com/cvjena/part_constellation_models)] [[Slides](https://cms.rz.uni-jena.de/dbvmedia/de/Simon/ICCV15_SimonRodner_slides.pdf)]

无链接