Model save
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README.md
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---
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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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metrics:
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- accuracy
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model-index:
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- name: vit-base-patch16-224-ethosrealdata
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results:
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- task:
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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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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.934010152284264
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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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should probably proofread and complete it, then remove this comment. -->
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# vit-base-patch16-224-ethosrealdata
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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: 0.2117
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- Accuracy: 0.9340
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 10
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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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| 0.9707 | 0.9913 | 57 | 0.6825 | 0.8160 |
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| 0.3507 | 2.0 | 115 | 0.3680 | 0.8909 |
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| 0.2002 | 2.9913 | 172 | 0.3121 | 0.9023 |
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| 0.1249 | 4.0 | 230 | 0.2951 | 0.9150 |
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| 0.1002 | 4.9913 | 287 | 0.2596 | 0.9251 |
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| 0.1014 | 6.0 | 345 | 0.2615 | 0.9251 |
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| 0.1261 | 6.9913 | 402 | 0.2437 | 0.9365 |
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| 0.0556 | 8.0 | 460 | 0.2198 | 0.9416 |
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| 0.0415 | 8.9913 | 517 | 0.2119 | 0.9416 |
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| 0.0294 | 9.9130 | 570 | 0.2117 | 0.9340 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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model.safetensors
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