CSAILVision/places365 resource

Health v2 · maintenance only

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2082 stars · 538 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Places365-CNNs is a collection of pretrained convolutional neural network models (AlexNet, GoogLeNet, VGG16, ResNet50/152) trained on the Places365 scene classification dataset of ~1.8 million images across 365 scene categories. It accompanies the larger Places2 database of 10+ million scene images from MIT CSAIL for scene recognition research.

Use cases

• classify images by scene category like bedroom, street, or kitchen
• extract deep scene features from photos for transfer learning
• benchmark scene recognition models against Places365 baselines
• generate scene attributes and class activation maps for images
• train models for indoor vs outdoor environment recognition
• use pretrained scene CNNs as a backbone for downstream vision tasks

When to choose

• you need scene/environment classification rather than object recognition
• you want pretrained CNN baselines for scene recognition research
• you need a large-scale scene dataset for training deep visual features
• you are reproducing Places2 Challenge or scene-centric benchmark results

When to avoid

• you need general object detection or fine-grained object classification
• you want a lightweight modern architecture rather than legacy Caffe/Torch models
• your project requires actively maintained code with recent framework support
• you need non-commercial licensing since the dataset is for academic research and education

Facets

dataset · maturity stable

machine-learning image-processing computer-vision computer-vision deep-learning image-processing machine-learning python cross-platform scene-classification pretrained-models cnn places365 scene-recognition caffe pytorch

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markdown · JSON · MCP: product_card(name="CSAILVision/places365")