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--- |
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license: apache-2.0 |
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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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metrics: |
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- accuracy |
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model-index: |
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- name: croupier-creature-classifier |
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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: croupier-mtg-dataset |
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type: imagefolder |
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config: alkzar90--croupier-mtg-dataset |
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split: train |
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args: alkzar90--croupier-mtg-dataset |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6839080459770115 |
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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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# croupier-creature-classifier |
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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 croupier-mtg-dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1036 |
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- Accuracy: 0.6839 |
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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: 8 |
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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: 25 |
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- mixed_precision_training: Native AMP |
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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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| 1.1638 | 1.1 | 100 | 1.0564 | 0.5471 | |
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| 0.8524 | 2.2 | 200 | 0.9403 | 0.6118 | |
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| 0.8231 | 3.3 | 300 | 0.8282 | 0.7176 | |
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| 0.7398 | 4.4 | 400 | 0.9056 | 0.6294 | |
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| 0.41 | 5.49 | 500 | 0.8815 | 0.6235 | |
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| 0.4849 | 6.59 | 600 | 0.9505 | 0.6294 | |
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| 0.3894 | 7.69 | 700 | 0.8052 | 0.6882 | |
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| 0.4678 | 8.79 | 800 | 0.8424 | 0.7059 | |
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| 0.4279 | 9.89 | 900 | 0.9639 | 0.6706 | |
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| 0.3461 | 10.99 | 1000 | 0.8497 | 0.7059 | |
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| 0.2741 | 12.09 | 1100 | 0.9090 | 0.7 | |
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| 0.1771 | 13.19 | 1200 | 0.8292 | 0.7118 | |
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| 0.1779 | 14.29 | 1300 | 1.1314 | 0.6294 | |
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| 0.2044 | 15.38 | 1400 | 0.8349 | 0.7294 | |
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| 0.1543 | 16.48 | 1500 | 0.8952 | 0.6941 | |
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| 0.1283 | 17.58 | 1600 | 0.8054 | 0.7353 | |
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| 0.1721 | 18.68 | 1700 | 0.9094 | 0.7235 | |
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| 0.1509 | 19.78 | 1800 | 0.9168 | 0.7412 | |
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| 0.1257 | 20.88 | 1900 | 0.9395 | 0.7412 | |
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| 0.1747 | 21.98 | 2000 | 0.8746 | 0.7471 | |
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| 0.1506 | 23.08 | 2100 | 0.7992 | 0.7353 | |
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| 0.1021 | 24.18 | 2200 | 0.7446 | 0.7706 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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