metadata

license: cc-by-nc-4.0
language:
  - en
pipeline_tag: zero-shot-image-classification
widget:
  - src: https://huggingface.co./lhaas/StreetCLIP/resolve/main/nagasaki.jpg
    candidate_labels: China, South Korea, Japan, Phillipines, Taiwan, Vietnam, Cambodia
    example_title: Countries
  - src: https://huggingface.co./lhaas/StreetCLIP/resolve/main/sanfrancisco.jpeg
    candidate_labels: San Jose, San Diego, Los Angeles, Las Vegas, San Francisco, Seattle
    example_title: Cities
  - src: https://huggingface.co./lhaas/StreetCLIP/resolve/main/australia.jpeg
    candidate_labels: >-
      tropical climate, dry climate, temperate climate, continental climate,
      polar climate
    example_title: Climate
library_name: transformers
tags:
  - geolocalization
  - geolocation
  - geographic
  - street
  - climate
  - clip
  - urban
  - rural

Model Card for Model ID

Model Details

Model Description

Developed by: Authors not disclosed
Model type: CLIP
Language: English
License: Create Commons Attribution Non Commercial 4.0
Finetuned from model: openai/clip-vit-large-patch14-336

Model Sources

Paper: Pre-print available soon ..
Demo: Currently in development ...

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

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Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from PIL import Image
import requests

from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("lhaas/StreetCLIP")
processor = CLIPProcessor.from_pretrained("lhaas/StreetCLIP")

url = "https://huggingface.co./lhaas/StreetCLIP/resolve/main/sanfrancisco.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

choices = ["San Jose", "San Diego", "Los Angeles", "Las Vegas", "San Francisco"]
inputs = processor(text=choices, images=image, return_tensors="pt", padding=True)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities

Training Details

Training Data

[More Information Needed]

Training Procedure [optional]

Preprocessing

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Speeds, Sizes, Times

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: 4 NVIDIA A100 GPUs
Hours used: 12

Example Image Attribution

[More information needed]

Citation [optional]

BibTeX:

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APA:

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