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README.md

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+---
+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
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+# Model Details
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+- **Developed by:** Authors not disclosed
+- **Model type:** [CLIP](https://openai.com/blog/clip/)
+- **Language:** English
+- **License:** Create Commons Attribution Non Commercial 4.0
+- **Finetuned from model:** [openai/clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336)
+
+## Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Paper:** Pre-print available soon ..
+- **Demo:** Currently in development ...
+
+# Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+## Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+## Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+## Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+# Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+## Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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.
+
+```python
+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
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+## Training Procedure [optional]
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+### Preprocessing
+
+[More Information Needed]
+
+### Speeds, Sizes, Times
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+# Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+## Testing Data, Factors & Metrics
+
+### Testing Data
+
+<!-- This should link to a Data Card if possible. -->
+
+[More Information Needed]
+
+### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+## Results
+
+[More Information Needed]
+
+### Summary
+
+
+
+# Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+# Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** 4 NVIDIA A100 GPUs
+- **Hours used:** 12
+
+# Example Image Attribution
+
+[More information needed]
+
+# Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]