--- library_name: transformers license: apache-2.0 base_model: Melo1512/vit-msn-small-wbc-classifier-0316-cleandataset-10 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-msn-small-wbc-classifier-0316-cleandataset-10 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8599162542824514 --- # vit-msn-small-wbc-classifier-0316-cleandataset-10 This model is a fine-tuned version of [Melo1512/vit-msn-small-wbc-classifier-0316-cleandataset-10](https://huggingface.co./Melo1512/vit-msn-small-wbc-classifier-0316-cleandataset-10) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3943 - Accuracy: 0.8599 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.3785 | 0.9730 | 18 | 0.3985 | 0.8569 | | 0.3432 | 2.0 | 37 | 0.3996 | 0.8557 | | 0.3454 | 2.9730 | 55 | 0.4011 | 0.8553 | | 0.3639 | 4.0 | 74 | 0.4034 | 0.8538 | | 0.3544 | 4.9730 | 92 | 0.4049 | 0.8546 | | 0.3607 | 6.0 | 111 | 0.4057 | 0.8538 | | 0.3652 | 6.9730 | 129 | 0.4046 | 0.8561 | | 0.3639 | 8.0 | 148 | 0.4046 | 0.8553 | | 0.3472 | 8.9730 | 166 | 0.4048 | 0.8561 | | 0.3704 | 10.0 | 185 | 0.4033 | 0.8546 | | 0.3954 | 10.9730 | 203 | 0.4009 | 0.8565 | | 0.372 | 12.0 | 222 | 0.4022 | 0.8546 | | 0.3599 | 12.9730 | 240 | 0.4005 | 0.8561 | | 0.3689 | 14.0 | 259 | 0.4018 | 0.8550 | | 0.3687 | 14.9730 | 277 | 0.4016 | 0.8553 | | 0.3521 | 16.0 | 296 | 0.4000 | 0.8561 | | 0.3817 | 16.9730 | 314 | 0.4001 | 0.8553 | | 0.3768 | 18.0 | 333 | 0.3994 | 0.8550 | | 0.3835 | 18.9730 | 351 | 0.4041 | 0.8546 | | 0.3833 | 20.0 | 370 | 0.4042 | 0.8553 | | 0.36 | 20.9730 | 388 | 0.4012 | 0.8561 | | 0.3729 | 22.0 | 407 | 0.4023 | 0.8565 | | 0.3647 | 22.9730 | 425 | 0.4029 | 0.8546 | | 0.3811 | 24.0 | 444 | 0.4011 | 0.8561 | | 0.38 | 24.9730 | 462 | 0.3999 | 0.8569 | | 0.3588 | 26.0 | 481 | 0.3994 | 0.8557 | | 0.3554 | 26.9730 | 499 | 0.3991 | 0.8561 | | 0.354 | 28.0 | 518 | 0.3995 | 0.8561 | | 0.3577 | 28.9730 | 536 | 0.3986 | 0.8557 | | 0.3723 | 30.0 | 555 | 0.3998 | 0.8561 | | 0.3763 | 30.9730 | 573 | 0.3994 | 0.8561 | | 0.3701 | 32.0 | 592 | 0.3994 | 0.8569 | | 0.3728 | 32.9730 | 610 | 0.3980 | 0.8553 | | 0.3649 | 34.0 | 629 | 0.3964 | 0.8565 | | 0.3551 | 34.9730 | 647 | 0.3982 | 0.8569 | | 0.3832 | 36.0 | 666 | 0.3977 | 0.8576 | | 0.3459 | 36.9730 | 684 | 0.3968 | 0.8561 | | 0.3613 | 38.0 | 703 | 0.3966 | 0.8561 | | 0.3588 | 38.9730 | 721 | 0.3968 | 0.8565 | | 0.3483 | 40.0 | 740 | 0.3958 | 0.8573 | | 0.3693 | 40.9730 | 758 | 0.3967 | 0.8576 | | 0.3544 | 42.0 | 777 | 0.3988 | 0.8576 | | 0.3701 | 42.9730 | 795 | 0.3976 | 0.8573 | | 0.3649 | 44.0 | 814 | 0.3984 | 0.8565 | | 0.3621 | 44.9730 | 832 | 0.3966 | 0.8573 | | 0.3494 | 46.0 | 851 | 0.3989 | 0.8573 | | 0.373 | 46.9730 | 869 | 0.3993 | 0.8573 | | 0.3911 | 48.0 | 888 | 0.3978 | 0.8576 | | 0.3716 | 48.9730 | 906 | 0.3967 | 0.8576 | | 0.3685 | 50.0 | 925 | 0.3968 | 0.8576 | | 0.3879 | 50.9730 | 943 | 0.3950 | 0.8573 | | 0.3774 | 52.0 | 962 | 0.3951 | 0.8580 | | 0.3588 | 52.9730 | 980 | 0.3950 | 0.8584 | | 0.3746 | 54.0 | 999 | 0.3959 | 0.8584 | | 0.3677 | 54.9730 | 1017 | 0.3960 | 0.8584 | | 0.3608 | 56.0 | 1036 | 0.3965 | 0.8588 | | 0.3518 | 56.9730 | 1054 | 0.3963 | 0.8580 | | 0.3554 | 58.0 | 1073 | 0.3957 | 0.8588 | | 0.3584 | 58.9730 | 1091 | 0.3957 | 0.8584 | | 0.3776 | 60.0 | 1110 | 0.3948 | 0.8592 | | 0.364 | 60.9730 | 1128 | 0.3942 | 0.8588 | | 0.3647 | 62.0 | 1147 | 0.3942 | 0.8584 | | 0.3613 | 62.9730 | 1165 | 0.3949 | 0.8588 | | 0.3509 | 64.0 | 1184 | 0.3961 | 0.8584 | | 0.3816 | 64.9730 | 1202 | 0.3967 | 0.8584 | | 0.3552 | 66.0 | 1221 | 0.3957 | 0.8588 | | 0.3461 | 66.9730 | 1239 | 0.3946 | 0.8588 | | 0.364 | 68.0 | 1258 | 0.3940 | 0.8588 | | 0.372 | 68.9730 | 1276 | 0.3943 | 0.8599 | | 0.347 | 70.0 | 1295 | 0.3939 | 0.8592 | | 0.3537 | 70.9730 | 1313 | 0.3943 | 0.8599 | | 0.3537 | 72.0 | 1332 | 0.3950 | 0.8595 | | 0.3823 | 72.9730 | 1350 | 0.3951 | 0.8592 | | 0.3454 | 74.0 | 1369 | 0.3947 | 0.8592 | | 0.3667 | 74.9730 | 1387 | 0.3949 | 0.8592 | | 0.3585 | 76.0 | 1406 | 0.3945 | 0.8592 | | 0.356 | 76.9730 | 1424 | 0.3947 | 0.8592 | | 0.337 | 78.0 | 1443 | 0.3949 | 0.8592 | | 0.3588 | 78.9730 | 1461 | 0.3944 | 0.8592 | | 0.3591 | 80.0 | 1480 | 0.3941 | 0.8592 | | 0.3638 | 80.9730 | 1498 | 0.3943 | 0.8592 | | 0.367 | 82.0 | 1517 | 0.3941 | 0.8592 | | 0.3694 | 82.9730 | 1535 | 0.3943 | 0.8592 | | 0.3779 | 84.0 | 1554 | 0.3941 | 0.8592 | | 0.344 | 84.9730 | 1572 | 0.3939 | 0.8595 | | 0.3619 | 86.0 | 1591 | 0.3935 | 0.8592 | | 0.342 | 86.9730 | 1609 | 0.3934 | 0.8595 | | 0.3686 | 88.0 | 1628 | 0.3931 | 0.8595 | | 0.3407 | 88.9730 | 1646 | 0.3931 | 0.8595 | | 0.3553 | 90.0 | 1665 | 0.3933 | 0.8599 | | 0.367 | 90.9730 | 1683 | 0.3934 | 0.8595 | | 0.3665 | 92.0 | 1702 | 0.3932 | 0.8599 | | 0.3684 | 92.9730 | 1720 | 0.3932 | 0.8599 | | 0.3685 | 94.0 | 1739 | 0.3934 | 0.8595 | | 0.375 | 94.9730 | 1757 | 0.3934 | 0.8592 | | 0.3564 | 96.0 | 1776 | 0.3934 | 0.8592 | | 0.362 | 96.9730 | 1794 | 0.3934 | 0.8592 | | 0.3688 | 97.2973 | 1800 | 0.3934 | 0.8592 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.2.0 - Tokenizers 0.19.1