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--- |
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library_name: transformers |
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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: 0.5625 |
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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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# 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: 1.2386 |
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- Accuracy: 0.5625 |
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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: 5e-05 |
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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: 30 |
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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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| 2.0874 | 1.0 | 10 | 2.0621 | 0.2313 | |
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| 2.036 | 2.0 | 20 | 2.0392 | 0.2375 | |
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| 1.9297 | 3.0 | 30 | 1.9592 | 0.3 | |
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| 1.7723 | 4.0 | 40 | 1.7877 | 0.3937 | |
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| 1.6184 | 5.0 | 50 | 1.6475 | 0.45 | |
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| 1.5407 | 6.0 | 60 | 1.5514 | 0.4875 | |
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| 1.4197 | 7.0 | 70 | 1.4967 | 0.4938 | |
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| 1.3092 | 8.0 | 80 | 1.4332 | 0.4813 | |
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| 1.1251 | 9.0 | 90 | 1.4457 | 0.4688 | |
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| 1.2081 | 10.0 | 100 | 1.3603 | 0.4938 | |
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| 0.9803 | 11.0 | 110 | 1.3501 | 0.5188 | |
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| 1.0105 | 12.0 | 120 | 1.3212 | 0.55 | |
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| 0.9264 | 13.0 | 130 | 1.2895 | 0.575 | |
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| 0.9229 | 14.0 | 140 | 1.2882 | 0.5188 | |
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| 0.9397 | 15.0 | 150 | 1.4027 | 0.475 | |
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| 0.8322 | 16.0 | 160 | 1.2824 | 0.5312 | |
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| 0.8185 | 17.0 | 170 | 1.3025 | 0.5 | |
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| 0.7592 | 18.0 | 180 | 1.3629 | 0.475 | |
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| 0.7416 | 19.0 | 190 | 1.3221 | 0.5437 | |
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| 0.6323 | 20.0 | 200 | 1.2714 | 0.5563 | |
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| 0.6453 | 21.0 | 210 | 1.3015 | 0.4938 | |
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| 0.6049 | 22.0 | 220 | 1.3065 | 0.5375 | |
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| 0.5919 | 23.0 | 230 | 1.2579 | 0.5375 | |
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| 0.5354 | 24.0 | 240 | 1.2428 | 0.55 | |
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| 0.6379 | 25.0 | 250 | 1.2884 | 0.5375 | |
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| 0.5681 | 26.0 | 260 | 1.2201 | 0.5938 | |
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| 0.4275 | 27.0 | 270 | 1.3199 | 0.4875 | |
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| 0.4791 | 28.0 | 280 | 1.3027 | 0.5312 | |
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| 0.4693 | 29.0 | 290 | 1.3737 | 0.4813 | |
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| 0.5528 | 30.0 | 300 | 1.3342 | 0.4688 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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