---
license: other
license_name: stem.ai.mtl
license_link: LICENSE
tags:
- vision
- image-classification
- STEM-AI-mtl/City_map
- Google
- ViT
- STEM-AI-mtl
datasets:
- STEM-AI-mtl/City_map

widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
  example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
  example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
  example_title: Palace
---

# The fine-tuned ViT model that beats [Google's base model](https://huggingface.co/google/vit-base-patch16-224) and OpenAI's GPT4

Image-classification model that identifies which city map is illustrated from an image input.

## Model description

The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.

By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.


### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

```python
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import requests

url = 'https://assets.wfcdn.com/im/16661612/compr-r85/4172/41722749/new-york-city-map-on-paper-print.jpg'
image = Image.open(requests.get(url, stream=True).raw)

processor = ViTImageProcessor.from_pretrained('STEM-AI-mtl/City_map-vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('STEM-AI-mtl/City_map-vit-base-patch16-224')

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits

predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```

For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).

## Training data

This [Google's ViT-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) model was fine-tuned on the [STEM-AI-mtl/City_map dataset](https://huggingface.co/datasets/STEM-AI-mtl/City_map), contaning overer 600 images of 45 different maps of cities around the world. 

## Training procedure


## Training evaluation results

The quality of the training was evaluated with the training dataset and resulted in the following metrics:\

{'eval_loss': 1.3691096305847168,\
 'eval_accuracy': 0.6666666666666666,\
 'eval_runtime': 13.0277,\
 'eval_samples_per_second': 4.606,\
 'eval_steps_per_second': 0.154,\
 'epoch': 2.82}


## Model Card Authors

STEM.AI: stem.ai.mtl@gmail.com\
[William Harbec](https://www.linkedin.com/in/william-harbec-56a262248/)