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README.md
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- image-classification
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- generated_from_trainer
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datasets:
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- renovation
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name: Image Classification
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type: image-classification
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dataset:
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name:
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type: renovation
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config: default
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split: validation
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-renovation
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the
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It achieves the following results on the evaluation set:
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- Loss:
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- Accuracy: 0.
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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### Framework versions
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- generated_from_trainer
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datasets:
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- renovation
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name: Image Classification
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type: image-classification
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dataset:
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name: renovation
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type: renovation
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config: default
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split: validation
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.6470588235294118
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-renovation
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the renovation dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2346
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- Accuracy: 0.6471
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 8
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.9092 | 1.67 | 100 | 0.8281 | 0.5686 |
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| 0.3809 | 3.33 | 200 | 0.7651 | 0.6667 |
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| 0.1873 | 5.0 | 300 | 1.0182 | 0.6667 |
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| 0.019 | 6.67 | 400 | 1.2346 | 0.6471 |
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### Framework versions
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