2.08 GB
8 files
Updated about 2 months ago
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| Name | Size | Uploaded | Xet hash |
|---|---|---|---|
| .gitattributes | 1.52 kB xet | 818ba6de | |
| README.md | 1.11 kB xet | 8b5b3906 | |
| beautiful.pth | 347 MB xet | b429dfa8 | |
| boring.pth | 347 MB xet | aad8f1fe | |
| depressing.pth | 347 MB xet | c63d19e0 | |
| lively.pth | 347 MB xet | 212da661 | |
| safety.pth | 347 MB xet | 12dde537 | |
| wealthy.pth | 347 MB xet | 243ade59 |
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