| ---
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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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| - imagefolder
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| metrics:
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| - accuracy
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| model-index:
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| - name: image_classification
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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: 0.51875
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| ---
|
|
|
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| should probably proofread and complete it, then remove this comment. -->
|
|
|
| # image_classification
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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 imagefolder dataset.
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| It achieves the following results on the evaluation set:
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| - Loss: 1.2566
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| - Accuracy: 0.5188
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|
|
| ## Model description
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|
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| More information needed
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|
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| ## Intended uses & limitations
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|
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| More information needed
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|
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| ## Training and evaluation data
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|
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| More information needed
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|
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| ## Training procedure
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|
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| ### Training hyperparameters
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|
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| The following hyperparameters were used during training:
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| - learning_rate: 5e-05
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| - train_batch_size: 32
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| - eval_batch_size: 16
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| - seed: 42
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| - optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
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| - lr_scheduler_type: linear
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| - num_epochs: 10
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|
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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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| | No log | 1.0 | 20 | 1.4431 | 0.3875 |
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| | No log | 2.0 | 40 | 1.4023 | 0.425 |
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| | No log | 3.0 | 60 | 1.3637 | 0.4437 |
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| | No log | 4.0 | 80 | 1.3424 | 0.4938 |
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| | No log | 5.0 | 100 | 1.3437 | 0.45 |
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| | No log | 6.0 | 120 | 1.3696 | 0.4938 |
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| | No log | 7.0 | 140 | 1.3172 | 0.4688 |
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| | No log | 8.0 | 160 | 1.2781 | 0.5125 |
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| | No log | 9.0 | 180 | 1.2599 | 0.5312 |
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| | No log | 10.0 | 200 | 1.3174 | 0.4813 |
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|
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| ### Framework versions
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|
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| - Transformers 4.41.2
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| - Pytorch 2.3.0
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| - Datasets 2.19.1
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| - Tokenizers 0.19.1
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| |
