We release various convolutional neural networks (CNNs) trained on Places365 to the public. Places365 is the latest subset of Places2 Database. There are two versions of Places365: Places365-Standard and Places365-Challenge. The train set of Places365-Standard has ~1.8 million images from 365 scene categories, where there are at most 5000 images per category. We have trained various baseline CNNs on the Places365-Standard and released them as below. Meanwhile, the train set of Places365-Challenge has extra 6.2 million images along with all the images of Places365-Standard (so totally ~8 million images), where there are at most 40,000 images per category. Places365-Challenge will be used for the Places2 Challenge 2016 to be held in conjunction with the ILSVRC and COCO joint workshop at ECCV 2016.
Places365-Standard and Places365-Challenge will be released at Places2 website soon.
- AlexNet:
deploy_alexnet_places365.prototxtweights:[http://places2.csail.mit.edu/models_places365/alexnet_places365.caffemodel] - GoogLeNet:
deploy_googlenet_places365.prototxtweights:[http://places2.csail.mit.edu/models_places365/googlenet_places365.caffemodel] - VGG16:
deploy_vgg16_places365.prototxtweights:[http://places2.csail.mit.edu/models_places365/vgg16_places365.caffemodel] - VGG16-hybrid1365:
deploy_vgg16_hybrid.prototxtweights:[http://places2.csail.mit.edu/models_places365/vgg16_hybrid.caffemodel]
The category index file is categories_places365.txt. Here we combine the training set of ImageNet 1.2 million data with Places365-Standard to train VGG16-hybrid1365 model, its category index file is categories_hybrid1365.txt. To download all the files, you could access here
The performance of the baseline CNNs is listed below. We use the class score averaged over 10-crops of each testing image to classify. <img src="http://places2.csail.mit.edu/models_places365/table2.jpg" alt="Drawing"/ style="height: 200px;"/>
As comparison, we list the performance of the baseline CNNs trained on Places205 as below. There are 160 more scene categories in Places365 than the Places205, the top-5 accuracy doesn't drop much. <img src="http://places2.csail.mit.edu/models_places365/table1.jpg" alt="Drawing"/ style="height: 250px;"/>
The performance of the deep features of Places365-CNNs as generic visual features is listed below. The setup for each experiment is the same as the ones in our NIPS'14 paper
Some qualitative prediction results using the VGG16-Places365:
Link: Places2 Database, Places1 Database
Please cite the following paper if you use the pre-trained CNN models.
Places2:A Large-scale Database for Scene Understanding
B. Zhou, A. Khosla, A. Lapedriza, A. Torralba and A. Oliva
Arxiv, 2016 (pdf coming soon)