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Coelhomatias
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deit-cvc-drop-aug

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deit
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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-base-distilled-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: deit-cvc-drop-aug
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+ results: []
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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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+ # deit-cvc-drop-aug
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+
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+ This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4769
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+ - Precision: 0.8894
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+ - Recall: 0.8064
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+ - F1: 0.8458
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+ - Accuracy: 0.8489
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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: 0.0001
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 17
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+ - gradient_accumulation_steps: 6
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+ - total_train_batch_size: 96
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 15
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.5453 | 0.27 | 100 | 0.4824 | 0.7776 | 0.7726 | 0.7751 | 0.7696 |
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+ | 0.4324 | 0.54 | 200 | 0.4796 | 0.8033 | 0.7279 | 0.7637 | 0.7686 |
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+ | 0.4042 | 0.82 | 300 | 0.3790 | 0.7697 | 0.9608 | 0.8547 | 0.8321 |
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+ | 0.3849 | 1.09 | 400 | 0.4100 | 0.8125 | 0.8198 | 0.8161 | 0.8102 |
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+ | 0.3621 | 1.36 | 500 | 0.3689 | 0.8099 | 0.8967 | 0.8511 | 0.8387 |
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+ | 0.3457 | 1.63 | 600 | 0.3313 | 0.7896 | 0.9543 | 0.8642 | 0.8459 |
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+ | 0.3443 | 1.9 | 700 | 0.3424 | 0.7836 | 0.9528 | 0.8600 | 0.8405 |
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+ | 0.3287 | 2.18 | 800 | 0.3308 | 0.8206 | 0.8947 | 0.8561 | 0.8454 |
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+ | 0.3224 | 2.45 | 900 | 0.4546 | 0.8481 | 0.6624 | 0.7438 | 0.7655 |
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+ | 0.3096 | 2.72 | 1000 | 0.3402 | 0.8300 | 0.8754 | 0.8521 | 0.8438 |
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+ | 0.3095 | 2.99 | 1100 | 0.3691 | 0.8035 | 0.9076 | 0.8524 | 0.8385 |
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+ | 0.2901 | 3.27 | 1200 | 0.3643 | 0.8008 | 0.8982 | 0.8467 | 0.8329 |
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+ | 0.2939 | 3.54 | 1300 | 0.3021 | 0.8047 | 0.9613 | 0.8760 | 0.8602 |
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+ | 0.2946 | 3.81 | 1400 | 0.3617 | 0.8363 | 0.8322 | 0.8342 | 0.8301 |
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+ | 0.2856 | 4.08 | 1500 | 0.4884 | 0.8401 | 0.7850 | 0.8116 | 0.8127 |
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+ | 0.2683 | 4.35 | 1600 | 0.3540 | 0.841 | 0.8352 | 0.8381 | 0.8341 |
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+ | 0.2724 | 4.63 | 1700 | 0.3078 | 0.8391 | 0.8957 | 0.8665 | 0.8581 |
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+ | 0.2685 | 4.9 | 1800 | 0.2913 | 0.8455 | 0.8967 | 0.8704 | 0.8627 |
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+ | 0.2449 | 5.17 | 1900 | 0.3866 | 0.8465 | 0.8515 | 0.8490 | 0.8443 |
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+ | 0.2468 | 5.44 | 2000 | 0.3072 | 0.8406 | 0.8952 | 0.8670 | 0.8589 |
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+ | 0.2557 | 5.71 | 2100 | 0.3735 | 0.8595 | 0.7900 | 0.8233 | 0.8257 |
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+ | 0.25 | 5.99 | 2200 | 0.3117 | 0.8755 | 0.8376 | 0.8561 | 0.8553 |
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+ | 0.2256 | 6.26 | 2300 | 0.3264 | 0.8407 | 0.8913 | 0.8653 | 0.8574 |
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+ | 0.234 | 6.53 | 2400 | 0.3617 | 0.8950 | 0.7572 | 0.8203 | 0.8295 |
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+ | 0.2259 | 6.8 | 2500 | 0.3284 | 0.8476 | 0.8893 | 0.8679 | 0.8609 |
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+ | 0.2261 | 7.07 | 2600 | 0.3486 | 0.9034 | 0.7805 | 0.8375 | 0.8443 |
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+ | 0.2087 | 7.35 | 2700 | 0.3971 | 0.8628 | 0.8118 | 0.8365 | 0.8369 |
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+ | 0.2035 | 7.62 | 2800 | 0.3106 | 0.8722 | 0.8942 | 0.8831 | 0.8783 |
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+ | 0.2116 | 7.89 | 2900 | 0.3734 | 0.8805 | 0.8083 | 0.8429 | 0.8451 |
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+ | 0.1956 | 8.16 | 3000 | 0.3443 | 0.8612 | 0.8654 | 0.8633 | 0.8591 |
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+ | 0.1826 | 8.44 | 3100 | 0.3795 | 0.8908 | 0.7900 | 0.8374 | 0.8423 |
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+ | 0.1918 | 8.71 | 3200 | 0.3362 | 0.8894 | 0.8267 | 0.8569 | 0.8581 |
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+ | 0.1886 | 8.98 | 3300 | 0.3259 | 0.8589 | 0.8798 | 0.8693 | 0.8640 |
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+ | 0.1716 | 9.25 | 3400 | 0.4269 | 0.8621 | 0.8347 | 0.8481 | 0.8464 |
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+ | 0.1654 | 9.52 | 3500 | 0.4066 | 0.8881 | 0.8317 | 0.8590 | 0.8597 |
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+ | 0.1625 | 9.8 | 3600 | 0.3927 | 0.8882 | 0.8128 | 0.8488 | 0.8512 |
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+ | 0.1659 | 10.07 | 3700 | 0.3797 | 0.8895 | 0.8193 | 0.8529 | 0.8548 |
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+ | 0.1519 | 10.34 | 3800 | 0.4089 | 0.8808 | 0.8217 | 0.8502 | 0.8512 |
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+ | 0.1484 | 10.61 | 3900 | 0.3865 | 0.8853 | 0.8237 | 0.8534 | 0.8546 |
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+ | 0.1427 | 10.88 | 4000 | 0.4347 | 0.8892 | 0.8009 | 0.8427 | 0.8464 |
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+ | 0.1375 | 11.16 | 4100 | 0.4688 | 0.8878 | 0.8213 | 0.8532 | 0.8548 |
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+ | 0.1276 | 11.43 | 4200 | 0.4687 | 0.8932 | 0.7974 | 0.8426 | 0.8469 |
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+ | 0.1275 | 11.7 | 4300 | 0.4493 | 0.8936 | 0.8009 | 0.8447 | 0.8487 |
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+ | 0.1349 | 11.97 | 4400 | 0.4618 | 0.8975 | 0.7825 | 0.8361 | 0.8423 |
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+ | 0.1217 | 12.24 | 4500 | 0.4636 | 0.8987 | 0.7974 | 0.8450 | 0.8497 |
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+ | 0.1211 | 12.52 | 4600 | 0.4527 | 0.8815 | 0.8307 | 0.8553 | 0.8556 |
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+ | 0.1164 | 12.79 | 4700 | 0.4669 | 0.8950 | 0.8123 | 0.8516 | 0.8546 |
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+ | 0.1119 | 13.06 | 4800 | 0.4617 | 0.8875 | 0.8148 | 0.8496 | 0.8517 |
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+ | 0.11 | 13.33 | 4900 | 0.4718 | 0.8894 | 0.8103 | 0.8480 | 0.8507 |
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+ | 0.1138 | 13.61 | 5000 | 0.4892 | 0.8939 | 0.7989 | 0.8437 | 0.8479 |
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+ | 0.1058 | 13.88 | 5100 | 0.4725 | 0.8875 | 0.8108 | 0.8474 | 0.8500 |
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+ | 0.1042 | 14.15 | 5200 | 0.4788 | 0.8908 | 0.8064 | 0.8465 | 0.8497 |
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+ | 0.107 | 14.42 | 5300 | 0.4759 | 0.8900 | 0.8073 | 0.8467 | 0.8497 |
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+ | 0.1047 | 14.69 | 5400 | 0.4767 | 0.8894 | 0.8064 | 0.8458 | 0.8489 |
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+ | 0.1085 | 14.97 | 5500 | 0.4769 | 0.8894 | 0.8064 | 0.8458 | 0.8489 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.2
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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