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HorcruxNo13
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beit-base-patch16-224

Image Classification
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beit
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  1. README.md +44 -51
  2. model.safetensors +1 -1
  3. runs/Mar27_16-25-26_dee86bb862cd/events.out.tfevents.1711556773.dee86bb862cd.484.0 +2 -2
  4. runs/Mar27_16-25-26_dee86bb862cd/events.out.tfevents.1711557673.dee86bb862cd.484.1 +3 -0
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3575
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- - Accuracy: 0.9456
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- - Precision: 0.9498
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- - Recall: 0.9456
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- - F1 Score: 0.9473
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  ## Model description
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@@ -43,11 +43,11 @@ More information needed
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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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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 128
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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
@@ -57,49 +57,42 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | No log | 0.94 | 4 | 0.3212 | 0.8475 | 0.8711 | 0.8475 | 0.7915 |
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- | No log | 1.88 | 8 | 0.2355 | 0.8983 | 0.8925 | 0.8983 | 0.8937 |
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- | No log | 2.82 | 12 | 0.3134 | 0.8644 | 0.8834 | 0.8644 | 0.8243 |
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- | 0.2493 | 4.0 | 17 | 0.2434 | 0.8814 | 0.8962 | 0.8814 | 0.8534 |
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- | 0.2493 | 4.94 | 21 | 0.3406 | 0.8983 | 0.9094 | 0.8983 | 0.8794 |
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- | 0.2493 | 5.88 | 25 | 0.1131 | 0.9322 | 0.9300 | 0.9322 | 0.9291 |
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- | 0.2493 | 6.82 | 29 | 0.1727 | 0.9153 | 0.9435 | 0.9153 | 0.9215 |
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- | 0.0374 | 8.0 | 34 | 0.6181 | 0.8644 | 0.8834 | 0.8644 | 0.8243 |
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- | 0.0374 | 8.94 | 38 | 0.3249 | 0.9153 | 0.9125 | 0.9153 | 0.9135 |
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- | 0.0374 | 9.88 | 42 | 0.5308 | 0.8983 | 0.8934 | 0.8983 | 0.8876 |
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- | 0.007 | 10.82 | 46 | 0.4767 | 0.9153 | 0.9119 | 0.9153 | 0.9090 |
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- | 0.007 | 12.0 | 51 | 0.3883 | 0.8983 | 0.8925 | 0.8983 | 0.8937 |
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- | 0.007 | 12.94 | 55 | 0.3627 | 0.8983 | 0.8934 | 0.8983 | 0.8876 |
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- | 0.007 | 13.88 | 59 | 0.2783 | 0.9492 | 0.9479 | 0.9492 | 0.9481 |
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- | 0.0012 | 14.82 | 63 | 0.1934 | 0.9492 | 0.9519 | 0.9492 | 0.9501 |
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- | 0.0012 | 16.0 | 68 | 0.1670 | 0.9661 | 0.9661 | 0.9661 | 0.9661 |
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- | 0.0012 | 16.94 | 72 | 0.1783 | 0.9492 | 0.9479 | 0.9492 | 0.9481 |
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- | 0.0001 | 17.88 | 76 | 0.4825 | 0.9322 | 0.9373 | 0.9322 | 0.9251 |
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- | 0.0001 | 18.82 | 80 | 0.9010 | 0.8983 | 0.9094 | 0.8983 | 0.8794 |
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- | 0.0001 | 20.0 | 85 | 0.1802 | 0.9661 | 0.9718 | 0.9661 | 0.9673 |
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- | 0.0001 | 20.94 | 89 | 0.5658 | 0.9153 | 0.9119 | 0.9153 | 0.9090 |
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- | 0.0037 | 21.88 | 93 | 0.8331 | 0.9322 | 0.9373 | 0.9322 | 0.9251 |
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- | 0.0037 | 22.82 | 97 | 0.8074 | 0.9153 | 0.9119 | 0.9153 | 0.9090 |
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- | 0.0037 | 24.0 | 102 | 0.4763 | 0.8814 | 0.8771 | 0.8814 | 0.8788 |
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- | 0.0002 | 24.94 | 106 | 0.5553 | 0.9153 | 0.9119 | 0.9153 | 0.9090 |
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- | 0.0002 | 25.88 | 110 | 0.8220 | 0.9153 | 0.9231 | 0.9153 | 0.9032 |
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- | 0.0002 | 26.82 | 114 | 0.5367 | 0.9322 | 0.9373 | 0.9322 | 0.9251 |
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- | 0.0002 | 28.0 | 119 | 0.4401 | 0.9153 | 0.9298 | 0.9153 | 0.9194 |
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- | 0.0037 | 28.94 | 123 | 0.4138 | 0.9153 | 0.9125 | 0.9153 | 0.9135 |
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- | 0.0037 | 29.88 | 127 | 0.7232 | 0.8983 | 0.9094 | 0.8983 | 0.8794 |
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- | 0.0037 | 30.82 | 131 | 0.3690 | 0.9322 | 0.9373 | 0.9322 | 0.9251 |
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- | 0.0115 | 32.0 | 136 | 0.2730 | 0.9322 | 0.9400 | 0.9322 | 0.9346 |
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- | 0.0115 | 32.94 | 140 | 0.2101 | 0.9661 | 0.9661 | 0.9661 | 0.9661 |
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- | 0.0115 | 33.88 | 144 | 0.1814 | 0.9661 | 0.9661 | 0.9661 | 0.9661 |
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- | 0.0115 | 34.82 | 148 | 0.1641 | 0.9661 | 0.9661 | 0.9661 | 0.9661 |
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- | 0.0013 | 36.0 | 153 | 0.1600 | 0.9492 | 0.9479 | 0.9492 | 0.9481 |
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- | 0.0013 | 36.94 | 157 | 0.1709 | 0.9661 | 0.9674 | 0.9661 | 0.9646 |
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- | 0.0013 | 37.88 | 161 | 0.1913 | 0.9661 | 0.9674 | 0.9661 | 0.9646 |
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- | 0.0001 | 38.82 | 165 | 0.2047 | 0.9661 | 0.9674 | 0.9661 | 0.9646 |
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- | 0.0001 | 40.0 | 170 | 0.2030 | 0.9661 | 0.9674 | 0.9661 | 0.9646 |
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- | 0.0001 | 40.94 | 174 | 0.1960 | 0.9661 | 0.9674 | 0.9661 | 0.9646 |
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- | 0.0001 | 41.88 | 178 | 0.1936 | 0.9661 | 0.9674 | 0.9661 | 0.9646 |
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- | 0.0003 | 42.35 | 180 | 0.1934 | 0.9661 | 0.9674 | 0.9661 | 0.9646 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5338
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+ - Accuracy: 0.7165
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+ - Precision: 0.7127
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+ - Recall: 0.7165
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+ - F1 Score: 0.7139
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  ## Model description
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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: 48
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+ - eval_batch_size: 48
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  - seed: 42
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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 192
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | No log | 0.8 | 2 | 0.7127 | 0.5686 | 0.3992 | 0.5686 | 0.4691 |
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+ | No log | 2.0 | 5 | 0.5967 | 0.6863 | 0.7053 | 0.6863 | 0.6139 |
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+ | No log | 2.8 | 7 | 0.5384 | 0.7843 | 0.7801 | 0.7843 | 0.7792 |
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+ | No log | 4.0 | 10 | 0.6429 | 0.6078 | 0.6547 | 0.6078 | 0.6164 |
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+ | No log | 4.8 | 12 | 0.6321 | 0.7255 | 0.7205 | 0.7255 | 0.7011 |
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+ | No log | 6.0 | 15 | 0.6473 | 0.7255 | 0.7164 | 0.7255 | 0.7095 |
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+ | No log | 6.8 | 17 | 0.7575 | 0.6863 | 0.6694 | 0.6863 | 0.6584 |
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+ | No log | 8.0 | 20 | 0.9926 | 0.7255 | 0.7312 | 0.7255 | 0.6908 |
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+ | No log | 8.8 | 22 | 0.9139 | 0.7255 | 0.7205 | 0.7255 | 0.7011 |
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+ | No log | 10.0 | 25 | 1.0884 | 0.7059 | 0.6937 | 0.7059 | 0.6845 |
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+ | No log | 10.8 | 27 | 1.2796 | 0.7451 | 0.7521 | 0.7451 | 0.7179 |
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+ | 0.287 | 12.0 | 30 | 1.3326 | 0.6863 | 0.6704 | 0.6863 | 0.6680 |
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+ | 0.287 | 12.8 | 32 | 1.5649 | 0.7255 | 0.7205 | 0.7255 | 0.7011 |
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+ | 0.287 | 14.0 | 35 | 1.7452 | 0.7255 | 0.7205 | 0.7255 | 0.7011 |
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+ | 0.287 | 14.8 | 37 | 1.7826 | 0.7255 | 0.7205 | 0.7255 | 0.7011 |
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+ | 0.287 | 16.0 | 40 | 1.9538 | 0.7255 | 0.7312 | 0.7255 | 0.6908 |
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+ | 0.287 | 16.8 | 42 | 1.8850 | 0.6863 | 0.6694 | 0.6863 | 0.6584 |
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+ | 0.287 | 18.0 | 45 | 1.7633 | 0.6863 | 0.6739 | 0.6863 | 0.6756 |
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+ | 0.287 | 18.8 | 47 | 1.7925 | 0.7059 | 0.6940 | 0.7059 | 0.6925 |
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+ | 0.287 | 20.0 | 50 | 2.1156 | 0.7255 | 0.7312 | 0.7255 | 0.6908 |
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+ | 0.287 | 20.8 | 52 | 2.0156 | 0.7255 | 0.7205 | 0.7255 | 0.7011 |
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+ | 0.287 | 22.0 | 55 | 1.8471 | 0.7255 | 0.7164 | 0.7255 | 0.7095 |
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+ | 0.287 | 22.8 | 57 | 1.7831 | 0.7647 | 0.7593 | 0.7647 | 0.7567 |
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+ | 0.0041 | 24.0 | 60 | 1.7628 | 0.7647 | 0.7593 | 0.7647 | 0.7567 |
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+ | 0.0041 | 24.8 | 62 | 1.8077 | 0.7451 | 0.7382 | 0.7451 | 0.7335 |
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+ | 0.0041 | 26.0 | 65 | 1.8068 | 0.7843 | 0.7823 | 0.7843 | 0.7745 |
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+ | 0.0041 | 26.8 | 67 | 1.7925 | 0.7647 | 0.7593 | 0.7647 | 0.7567 |
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+ | 0.0041 | 28.0 | 70 | 1.7721 | 0.7843 | 0.7823 | 0.7843 | 0.7745 |
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+ | 0.0041 | 28.8 | 72 | 1.7919 | 0.7647 | 0.7624 | 0.7647 | 0.7510 |
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+ | 0.0041 | 30.0 | 75 | 1.9588 | 0.7451 | 0.7521 | 0.7451 | 0.7179 |
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+ | 0.0041 | 30.8 | 77 | 1.9200 | 0.7451 | 0.7521 | 0.7451 | 0.7179 |
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+ | 0.0041 | 32.0 | 80 | 1.7746 | 0.7451 | 0.7521 | 0.7451 | 0.7179 |
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+ | 0.0041 | 32.8 | 82 | 1.7253 | 0.7647 | 0.7624 | 0.7647 | 0.7510 |
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+ | 0.0041 | 34.0 | 85 | 1.6992 | 0.7451 | 0.7382 | 0.7451 | 0.7335 |
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+ | 0.0041 | 34.8 | 87 | 1.6938 | 0.7451 | 0.7382 | 0.7451 | 0.7335 |
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+ | 0.0031 | 36.0 | 90 | 1.7014 | 0.7451 | 0.7382 | 0.7451 | 0.7335 |
 
 
 
 
 
 
 
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