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--- |
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license: other |
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license_name: stem.ai.mtl |
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license_link: LICENSE |
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tags: |
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- vision |
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- image-classification |
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- STEM-AI-mtl/City_map |
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- Google |
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- ViT |
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- STEM-AI-mtl |
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datasets: |
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- STEM-AI-mtl/City_map |
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--- |
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# The fine-tuned ViT model that beats [Google's state-of-the-art model](https://huggingface.co/google/vit-base-patch16-224) and OpenAI's famous GPT4 for maps of cities around the world |
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Image-classification fine-tuned model that identifies which city map is illustrated from an image input. |
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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. |
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- **Developed by:** STEM.AI |
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- **Model type:** Image classification of maps of cities |
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- **Finetuned from model:** [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) |
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### How to use: |
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[Inference script](https://github.com/STEM-ai/Vision/blob/7d92c8daa388eb74e8c336f2d0d3942722fec3c6/ViT_inference.py) |
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For more code examples, we refer to [ViTdocumentation](https://huggingface.co/transformers/model_doc/vit.html#). |
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## Training data |
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This [Google's ViT-base-patch16-224 for city identification](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. |
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## Training procedure |
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A Transformer training was performed on [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on a 4 Gb Nvidia GTX 1650 GPU. |
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[Training notebook](https://github.com/STEM-ai/Vision/raw/7d92c8daa388eb74e8c336f2d0d3942722fec3c6/Trainer_ViT.ipynb) |
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## Training evaluation results |
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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: |
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{'eval_loss': 1.3691096305847168,\ |
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'eval_accuracy': 0.6666666666666666,\ |
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'eval_runtime': 13.0277,\ |
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'eval_samples_per_second': 4.606,\ |
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'eval_steps_per_second': 0.154,\ |
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'epoch': 2.82} |
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## Model Card Authors |
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STEM.AI: stem.ai.mtl@gmail.com\ |
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[William Harbec](https://www.linkedin.com/in/william-harbec-56a262248/) |
