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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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This model is a finetuned image classificator based on [vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) |
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This model takes as input a picture from google maps' street view representing a road and returns a walkability score from 0 (worst score) to 4 (best score) |
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# How to Use |
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Load the model with the following code: |
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```python |
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from transformers import AutoModelForImageClassification |
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model = AutoModelForImageClassification.from_pretrained("AEnigmista/Sardegna-ViT", num_labels=5, ignore_mismatched_sizes=True) |
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``` |
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For more information on the code: please visit the [github repo](https://github.com/MatteoMocci/Most-Walkability-AI) |
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# Training Hyper-parameters |
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This version's hyper-parameters for training are: |
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- Fp16 = True |
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- batch size = 32 |
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- 10 epochs |
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- learning rate = 1e-4 |
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- optimizer = 'adamw_hf' |
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# Metrics |
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The metrics that are used for evaluation are accuracy, recall, precision, mse, confusion matrix and a custom metric called one_out. The one_out_accuracy uses |
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the confusion matrix to check how many predictions of the model are within 1 from the ground truth (so label 2 is considered correct if ground truth is 1 or 3, incorrect if 0 or 5). |
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Since each label is actually a walkability score, this metric is useful to see how many predictions of the model are correct or pretty close to the expected value, and, thus, |
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how many predictions are way off (for example a street with 0 walkability score is predicted as 4) |
