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smids_1x_deit_small_adamax_001_fold4

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  2. model.safetensors +1 -1
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/deit-small-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: smids_1x_deit_small_adamax_001_fold4
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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: test
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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: 0.8583333333333333
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+ ---
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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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+
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+ # smids_1x_deit_small_adamax_001_fold4
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+
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+ This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3725
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+ - Accuracy: 0.8583
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+
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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: 0.001
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.515 | 1.0 | 75 | 0.5041 | 0.8 |
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+ | 0.5037 | 2.0 | 150 | 0.5053 | 0.79 |
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+ | 0.2905 | 3.0 | 225 | 0.4325 | 0.8283 |
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+ | 0.3469 | 4.0 | 300 | 0.4033 | 0.845 |
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+ | 0.2428 | 5.0 | 375 | 0.5311 | 0.8033 |
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+ | 0.2048 | 6.0 | 450 | 0.4711 | 0.8467 |
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+ | 0.1912 | 7.0 | 525 | 0.5110 | 0.84 |
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+ | 0.1617 | 8.0 | 600 | 0.4469 | 0.845 |
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+ | 0.1268 | 9.0 | 675 | 0.6229 | 0.8483 |
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+ | 0.0586 | 10.0 | 750 | 0.6742 | 0.8433 |
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+ | 0.05 | 11.0 | 825 | 0.6876 | 0.8517 |
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+ | 0.0724 | 12.0 | 900 | 0.6762 | 0.8633 |
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+ | 0.0728 | 13.0 | 975 | 0.7911 | 0.8417 |
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+ | 0.0259 | 14.0 | 1050 | 0.6721 | 0.84 |
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+ | 0.0254 | 15.0 | 1125 | 0.7841 | 0.8517 |
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+ | 0.0264 | 16.0 | 1200 | 0.9642 | 0.8383 |
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+ | 0.0503 | 17.0 | 1275 | 0.9056 | 0.8483 |
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+ | 0.0537 | 18.0 | 1350 | 1.0301 | 0.8517 |
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+ | 0.0053 | 19.0 | 1425 | 0.9551 | 0.845 |
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+ | 0.0045 | 20.0 | 1500 | 0.9526 | 0.8483 |
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+ | 0.0133 | 21.0 | 1575 | 1.0780 | 0.8333 |
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+ | 0.0123 | 22.0 | 1650 | 0.9370 | 0.8617 |
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+ | 0.0051 | 23.0 | 1725 | 0.9638 | 0.855 |
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+ | 0.0056 | 24.0 | 1800 | 0.9925 | 0.8517 |
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+ | 0.0121 | 25.0 | 1875 | 1.0419 | 0.8483 |
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+ | 0.0002 | 26.0 | 1950 | 1.0739 | 0.8567 |
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+ | 0.0 | 27.0 | 2025 | 1.1470 | 0.8583 |
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+ | 0.0068 | 28.0 | 2100 | 1.1576 | 0.8583 |
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+ | 0.0055 | 29.0 | 2175 | 1.1500 | 0.8567 |
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+ | 0.0 | 30.0 | 2250 | 1.1994 | 0.8533 |
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+ | 0.0 | 31.0 | 2325 | 1.2151 | 0.8583 |
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+ | 0.0 | 32.0 | 2400 | 1.2684 | 0.8567 |
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+ | 0.0 | 33.0 | 2475 | 1.1310 | 0.8567 |
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+ | 0.0 | 34.0 | 2550 | 1.1896 | 0.855 |
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+ | 0.0 | 35.0 | 2625 | 1.2405 | 0.8567 |
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+ | 0.0004 | 36.0 | 2700 | 1.2637 | 0.8567 |
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+ | 0.0037 | 37.0 | 2775 | 1.2924 | 0.8567 |
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+ | 0.0 | 38.0 | 2850 | 1.3058 | 0.855 |
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+ | 0.0 | 39.0 | 2925 | 1.3179 | 0.855 |
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+ | 0.0001 | 40.0 | 3000 | 1.3267 | 0.855 |
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+ | 0.003 | 41.0 | 3075 | 1.3385 | 0.8583 |
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+ | 0.0 | 42.0 | 3150 | 1.3471 | 0.8583 |
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+ | 0.0 | 43.0 | 3225 | 1.3533 | 0.8583 |
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+ | 0.0 | 44.0 | 3300 | 1.3593 | 0.8583 |
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+ | 0.0 | 45.0 | 3375 | 1.3636 | 0.8583 |
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+ | 0.0 | 46.0 | 3450 | 1.3666 | 0.8583 |
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+ | 0.0 | 47.0 | 3525 | 1.3691 | 0.8583 |
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+ | 0.0 | 48.0 | 3600 | 1.3710 | 0.8583 |
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+ | 0.0 | 49.0 | 3675 | 1.3721 | 0.8583 |
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+ | 0.0 | 50.0 | 3750 | 1.3725 | 0.8583 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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