The fine-tuned ViT model that beats Google's state-of-the-art model and OpenAI's famous GPT4 for maps of cities around the world
Image-classification fine-tuned model that identifies which city map is illustrated from an image input.
The Vision Transformer (ViT) base model 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.
- Developed by: STEM.AI
- Model type: Image classification of maps of cities
- Finetuned from model: google/vit-base-patch16-224
How to use:
For more code examples, we refer to ViTdocumentation.
Training data
This Google's ViT-base-patch16-224 for city identification model was fine-tuned on the STEM-AI-mtl/City_map dataset, contaning overer 600 images of 45 different maps of cities around the world.
Training procedure
A Transformer training was performed on google/vit-base-patch16-224 on a 4 Gb Nvidia GTX 1650 GPU.
Training evaluation results
The most accurate output model was obtained from a learning rate of 1e-3. 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
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