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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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pipeline_tag: image-classification
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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-renovation2
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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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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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---
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### Framework versions
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- Transformers 4.
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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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: 0.7031963470319634
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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-renovation2
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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: 0.9788
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- Accuracy: 0.7032
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.359 | 0.2 | 25 | 1.2074 | 0.4658 |
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| 1.1384 | 0.4 | 50 | 1.1213 | 0.5205 |
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| 1.0866 | 0.6 | 75 | 0.9746 | 0.6301 |
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| 1.1787 | 0.81 | 100 | 1.0523 | 0.5662 |
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| 0.9242 | 1.01 | 125 | 0.9543 | 0.6256 |
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| 0.7945 | 1.21 | 150 | 0.9200 | 0.6119 |
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| 0.8379 | 1.41 | 175 | 0.8447 | 0.6712 |
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| 0.7253 | 1.61 | 200 | 0.8642 | 0.6575 |
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| 0.6344 | 1.81 | 225 | 0.8443 | 0.6438 |
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| 0.6521 | 2.02 | 250 | 0.8273 | 0.6667 |
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| 0.3627 | 2.22 | 275 | 0.8653 | 0.6712 |
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| 0.2523 | 2.42 | 300 | 0.8748 | 0.6895 |
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| 0.363 | 2.62 | 325 | 0.8407 | 0.6849 |
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| 0.3433 | 2.82 | 350 | 0.9696 | 0.6484 |
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| 0.2874 | 3.02 | 375 | 0.9290 | 0.6804 |
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| 0.1682 | 3.23 | 400 | 0.9713 | 0.6575 |
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| 0.1575 | 3.43 | 425 | 0.9963 | 0.6804 |
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| 0.0822 | 3.63 | 450 | 0.9473 | 0.7123 |
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| 0.1678 | 3.83 | 475 | 0.9788 | 0.7032 |
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### Framework versions
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- Transformers 4.39.1
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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model.safetensors
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runs/Mar22_21-16-36_9f0b864d5439/events.out.tfevents.1711142204.9f0b864d5439.318.0
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