2.08 GB
8 files
Updated about 2 months ago
Name
Size
.gitattributes1.52 kB
xet
README.md1.11 kB
xet
beautiful.pth347 MB
xet
boring.pth347 MB
xet
depressing.pth347 MB
xet
lively.pth347 MB
xet
safety.pth347 MB
xet
wealthy.pth347 MB
xet
README.md

human-perception-place-pulse

What does the model do

safety, lively, beautiful, wealthy, boring and depressing.

Getting human perception scores from street-level imagery.

The scores are in scale of 0-10.

Safety, lively, beautiful, wealthy high score indicates strong positive feeling

Boring, depressing high score indicates strong negative feeling

Model

The models are pre-trained on MIT Place Pulse 2.0 dataset. The backbone of the model is vision transformer pretrianed on ImageNet (ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1). 3 Linear layers are added in ViT heads for classification.

How to run the model

Install packages from requirements.txt

pip install -r requirements.txt

Change the file path in eval.py

model_load_path = "./model/"   # model path
images_path = "./test_image"      # your input image path
out_Path = "./output"     # output path

Run the file eval.py

python eval.py

References

Please refer to human-perception-place-pulse for details.

Total size
2.08 GB
Files
8
Last updated
Aug 15
Pre-warmed CDN
US EU US EU

Contributors