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resnet-50-linearhead-2024_03_12-with_data_aug_batch-size32_epochs93_freeze

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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: microsoft/resnet-50
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: resnet-50-linearhead-2024_03_12-with_data_aug_batch-size32_epochs93_freeze
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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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+ # resnet-50-linearhead-2024_03_12-with_data_aug_batch-size32_epochs93_freeze
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+
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+ This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1546
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+ - F1 Micro: 0.7452
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+ - F1 Macro: 0.6223
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+ - Roc Auc: 0.8208
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+ - Accuracy: 0.4050
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+ - Learning Rate: 1e-05
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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: 5e-05
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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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+ - num_epochs: 93
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:------:|
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+ | No log | 1.0 | 274 | 0.2237 | 0.5839 | 0.2834 | 0.7176 | 0.1952 | 0.001 |
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+ | 0.2683 | 2.0 | 548 | 0.1895 | 0.6773 | 0.4549 | 0.7743 | 0.3055 | 0.001 |
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+ | 0.2683 | 3.0 | 822 | 0.1786 | 0.7021 | 0.5202 | 0.7911 | 0.3539 | 0.001 |
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+ | 0.2058 | 4.0 | 1096 | 0.1715 | 0.7198 | 0.5666 | 0.8058 | 0.3667 | 0.001 |
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+ | 0.2058 | 5.0 | 1370 | 0.1662 | 0.7220 | 0.5718 | 0.8050 | 0.3768 | 0.001 |
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+ | 0.1916 | 6.0 | 1644 | 0.1648 | 0.7155 | 0.5721 | 0.7980 | 0.3796 | 0.001 |
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+ | 0.1916 | 7.0 | 1918 | 0.1618 | 0.7281 | 0.5973 | 0.8082 | 0.3810 | 0.001 |
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+ | 0.1858 | 8.0 | 2192 | 0.1598 | 0.7375 | 0.6061 | 0.8166 | 0.3855 | 0.001 |
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+ | 0.1858 | 9.0 | 2466 | 0.1599 | 0.7440 | 0.6209 | 0.8223 | 0.3911 | 0.001 |
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+ | 0.1839 | 10.0 | 2740 | 0.1584 | 0.7382 | 0.6047 | 0.8173 | 0.3949 | 0.001 |
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+ | 0.1815 | 11.0 | 3014 | 0.1569 | 0.7414 | 0.6068 | 0.8186 | 0.3960 | 0.001 |
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+ | 0.1815 | 12.0 | 3288 | 0.1585 | 0.7257 | 0.5953 | 0.8043 | 0.3963 | 0.001 |
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+ | 0.1807 | 13.0 | 3562 | 0.1581 | 0.7514 | 0.6286 | 0.8311 | 0.3967 | 0.001 |
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+ | 0.1807 | 14.0 | 3836 | 0.1565 | 0.7453 | 0.6230 | 0.8224 | 0.4022 | 0.001 |
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+ | 0.1795 | 15.0 | 4110 | 0.1549 | 0.7504 | 0.6253 | 0.8262 | 0.3991 | 0.001 |
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+ | 0.1795 | 16.0 | 4384 | 0.1573 | 0.7446 | 0.6290 | 0.8214 | 0.3939 | 0.001 |
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+ | 0.178 | 17.0 | 4658 | 0.1551 | 0.7519 | 0.6287 | 0.8281 | 0.4026 | 0.001 |
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+ | 0.178 | 18.0 | 4932 | 0.1570 | 0.7430 | 0.6155 | 0.8203 | 0.3914 | 0.001 |
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+ | 0.1764 | 19.0 | 5206 | 0.1558 | 0.7480 | 0.6287 | 0.8236 | 0.3991 | 0.001 |
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+ | 0.1764 | 20.0 | 5480 | 0.1574 | 0.7403 | 0.6085 | 0.8164 | 0.4001 | 0.001 |
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+ | 0.1775 | 21.0 | 5754 | 0.1561 | 0.7532 | 0.6246 | 0.8302 | 0.4029 | 0.001 |
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+ | 0.177 | 22.0 | 6028 | 0.1545 | 0.7596 | 0.6431 | 0.8378 | 0.3974 | 0.0001 |
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+ | 0.177 | 23.0 | 6302 | 0.1556 | 0.7472 | 0.6292 | 0.8233 | 0.4026 | 0.0001 |
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+ | 0.1762 | 24.0 | 6576 | 0.1548 | 0.7528 | 0.6343 | 0.8283 | 0.3994 | 0.0001 |
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+ | 0.1762 | 25.0 | 6850 | 0.1554 | 0.7468 | 0.6225 | 0.8222 | 0.3994 | 0.0001 |
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+ | 0.1759 | 26.0 | 7124 | 0.1548 | 0.7529 | 0.6326 | 0.8297 | 0.3977 | 0.0001 |
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+ | 0.1759 | 27.0 | 7398 | 0.1552 | 0.7516 | 0.6352 | 0.8282 | 0.3970 | 0.0001 |
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+ | 0.1752 | 28.0 | 7672 | 0.1543 | 0.7523 | 0.6328 | 0.8277 | 0.4092 | 0.0001 |
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+ | 0.1752 | 29.0 | 7946 | 0.1545 | 0.7506 | 0.6312 | 0.8265 | 0.4019 | 0.0001 |
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+ | 0.1757 | 30.0 | 8220 | 0.1550 | 0.7554 | 0.6394 | 0.8340 | 0.4040 | 0.0001 |
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+ | 0.1757 | 31.0 | 8494 | 0.1554 | 0.7512 | 0.6345 | 0.8279 | 0.4022 | 0.0001 |
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+ | 0.1758 | 32.0 | 8768 | 0.1545 | 0.7513 | 0.6302 | 0.8275 | 0.4033 | 0.0001 |
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+ | 0.1755 | 33.0 | 9042 | 0.1555 | 0.7456 | 0.6261 | 0.8223 | 0.3977 | 0.0001 |
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+ | 0.1755 | 34.0 | 9316 | 0.1533 | 0.7515 | 0.6307 | 0.8260 | 0.4109 | 0.0001 |
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+ | 0.1752 | 35.0 | 9590 | 0.1551 | 0.7506 | 0.6325 | 0.8261 | 0.4054 | 0.0001 |
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+ | 0.1752 | 36.0 | 9864 | 0.1530 | 0.7539 | 0.6299 | 0.8287 | 0.4026 | 0.0001 |
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+ | 0.1752 | 37.0 | 10138 | 0.1546 | 0.7464 | 0.6270 | 0.8223 | 0.4036 | 0.0001 |
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+ | 0.1752 | 38.0 | 10412 | 0.1549 | 0.7539 | 0.6364 | 0.8314 | 0.3987 | 0.0001 |
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+ | 0.1763 | 39.0 | 10686 | 0.1547 | 0.7579 | 0.6421 | 0.8361 | 0.3977 | 0.0001 |
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+ | 0.1763 | 40.0 | 10960 | 0.1544 | 0.7539 | 0.6345 | 0.8302 | 0.4005 | 0.0001 |
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+ | 0.176 | 41.0 | 11234 | 0.1557 | 0.7536 | 0.6347 | 0.8298 | 0.4015 | 0.0001 |
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+ | 0.1758 | 42.0 | 11508 | 0.1540 | 0.7474 | 0.6277 | 0.8226 | 0.3960 | 0.0001 |
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+ | 0.1758 | 43.0 | 11782 | 0.1548 | 0.7578 | 0.6384 | 0.8374 | 0.3970 | 1e-05 |
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+ | 0.1764 | 44.0 | 12056 | 0.1543 | 0.7582 | 0.6398 | 0.8352 | 0.4012 | 1e-05 |
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+ | 0.1764 | 45.0 | 12330 | 0.1544 | 0.7448 | 0.6206 | 0.8196 | 0.3991 | 1e-05 |
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+ | 0.1746 | 46.0 | 12604 | 0.1546 | 0.7452 | 0.6223 | 0.8208 | 0.4050 | 1e-05 |
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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 2.1.0+cu118
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+ - Datasets 2.14.5
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+ - Tokenizers 0.15.0
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