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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
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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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- imagefolder
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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: imagefolder
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config: default
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split: train
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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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- name: F1
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type: f1
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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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# Model
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) 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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- F1: 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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### Framework versions
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- Transformers 4.
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- Pytorch 2.1
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- Datasets 2.
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- Tokenizers 0.15.
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license: apache-2.0
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base_model: google/vit-base-patch16-224
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.3333333333333333
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- name: F1
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type: f1
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value: 0.16666666666666666
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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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# Model
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.2752
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- Accuracy: 0.3333
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- F1: 0.1667
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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: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 2.1596 | 50.0 | 50 | 4.2752 | 0.3333 | 0.1667 |
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### Framework versions
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- Transformers 4.39.0
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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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