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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 +53 -46
  2. model.safetensors +1 -1
  3. runs/May04_13-45-02_9f063c349a98/events.out.tfevents.1714830304.9f063c349a98.602.0 +2 -2
  4. runs/May04_13-45-02_9f063c349a98/events.out.tfevents.1714831605.9f063c349a98.602.1 +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: 0.8528
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- - Accuracy: 0.8268
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- - Precision: 0.8303
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- - Recall: 0.8268
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- - F1 Score: 0.8283
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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: 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
@@ -55,44 +55,51 @@ The following hyperparameters were used during training:
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  ### Training results
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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 | 0.6993 | 0.5882 | 0.5390 | 0.5882 | 0.5541 |
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- | No log | 2.0 | 5 | 0.5971 | 0.6863 | 0.6806 | 0.6863 | 0.6033 |
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- | No log | 2.8 | 7 | 0.5306 | 0.8039 | 0.8000 | 0.8039 | 0.8006 |
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- | No log | 4.0 | 10 | 0.4828 | 0.7255 | 0.7229 | 0.7255 | 0.6859 |
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- | No log | 4.8 | 12 | 0.3812 | 0.7843 | 0.7786 | 0.7843 | 0.7784 |
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- | 0.5413 | 6.0 | 15 | 0.5268 | 0.7451 | 0.7461 | 0.7451 | 0.7141 |
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- | 0.5413 | 6.8 | 17 | 0.5349 | 0.7451 | 0.8556 | 0.7451 | 0.7502 |
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- | 0.5413 | 8.0 | 20 | 0.4120 | 0.8039 | 0.8485 | 0.8039 | 0.7756 |
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- | 0.5413 | 8.8 | 22 | 0.3156 | 0.8039 | 0.8003 | 0.8039 | 0.7963 |
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- | 0.5413 | 10.0 | 25 | 0.3217 | 0.8039 | 0.8061 | 0.8039 | 0.7909 |
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- | 0.5413 | 10.8 | 27 | 0.5161 | 0.7843 | 0.7870 | 0.7843 | 0.7664 |
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- | 0.0919 | 12.0 | 30 | 0.3677 | 0.8431 | 0.8498 | 0.8431 | 0.8451 |
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- | 0.0919 | 12.8 | 32 | 0.4631 | 0.8431 | 0.8407 | 0.8431 | 0.8405 |
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- | 0.0919 | 14.0 | 35 | 0.5001 | 0.8235 | 0.8214 | 0.8235 | 0.8221 |
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- | 0.0919 | 14.8 | 37 | 0.4489 | 0.8431 | 0.8431 | 0.8431 | 0.8431 |
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- | 0.0919 | 16.0 | 40 | 0.5892 | 0.7843 | 0.7799 | 0.7843 | 0.7731 |
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- | 0.0919 | 16.8 | 42 | 0.6579 | 0.7843 | 0.7799 | 0.7843 | 0.7731 |
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- | 0.006 | 18.0 | 45 | 0.7038 | 0.7843 | 0.7799 | 0.7843 | 0.7731 |
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- | 0.006 | 18.8 | 47 | 0.5864 | 0.8627 | 0.8737 | 0.8627 | 0.8651 |
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- | 0.006 | 20.0 | 50 | 0.5488 | 0.8627 | 0.8737 | 0.8627 | 0.8651 |
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- | 0.006 | 20.8 | 52 | 0.6651 | 0.8039 | 0.8003 | 0.8039 | 0.7963 |
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- | 0.006 | 22.0 | 55 | 0.6265 | 0.8039 | 0.8000 | 0.8039 | 0.8006 |
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- | 0.006 | 22.8 | 57 | 0.5229 | 0.8627 | 0.8653 | 0.8627 | 0.8637 |
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- | 0.0048 | 24.0 | 60 | 0.5421 | 0.8627 | 0.8653 | 0.8627 | 0.8637 |
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- | 0.0048 | 24.8 | 62 | 0.6335 | 0.8235 | 0.8205 | 0.8235 | 0.8187 |
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- | 0.0048 | 26.0 | 65 | 1.0379 | 0.8039 | 0.8201 | 0.8039 | 0.7841 |
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- | 0.0048 | 26.8 | 67 | 0.9758 | 0.8235 | 0.8366 | 0.8235 | 0.8089 |
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- | 0.0048 | 28.0 | 70 | 0.6117 | 0.8235 | 0.8205 | 0.8235 | 0.8187 |
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- | 0.0048 | 28.8 | 72 | 0.5403 | 0.8627 | 0.8613 | 0.8627 | 0.8617 |
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- | 0.0063 | 30.0 | 75 | 0.6469 | 0.8431 | 0.8407 | 0.8431 | 0.8405 |
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- | 0.0063 | 30.8 | 77 | 0.7014 | 0.8235 | 0.8205 | 0.8235 | 0.8187 |
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- | 0.0063 | 32.0 | 80 | 0.7514 | 0.8235 | 0.8205 | 0.8235 | 0.8187 |
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- | 0.0063 | 32.8 | 82 | 0.7771 | 0.8235 | 0.8248 | 0.8235 | 0.8144 |
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- | 0.0063 | 34.0 | 85 | 0.7599 | 0.8039 | 0.8003 | 0.8039 | 0.7963 |
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- | 0.0063 | 34.8 | 87 | 0.7554 | 0.8039 | 0.8003 | 0.8039 | 0.7963 |
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- | 0.0045 | 36.0 | 90 | 0.7308 | 0.8039 | 0.8003 | 0.8039 | 0.7963 |
 
 
 
 
 
 
 
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  ### Framework versions
 
19
 
20
  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.
21
  It achieves the following results on the evaluation set:
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+ - Loss: 0.3752
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+ - Accuracy: 0.9388
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+ - Precision: 0.9451
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+ - Recall: 0.9388
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+ - F1 Score: 0.9412
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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: 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
 
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  ### Training results
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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 | 0.9412 | 4 | 0.3599 | 0.8644 | 0.8831 | 0.8644 | 0.8152 |
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+ | No log | 1.8824 | 8 | 0.2752 | 0.8983 | 0.8983 | 0.8983 | 0.8983 |
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+ | No log | 2.8235 | 12 | 0.1735 | 0.9322 | 0.9293 | 0.9322 | 0.9286 |
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+ | 0.2978 | 4.0 | 17 | 0.1745 | 0.9153 | 0.9311 | 0.9153 | 0.9200 |
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+ | 0.2978 | 4.9412 | 21 | 0.1888 | 0.9153 | 0.9196 | 0.9153 | 0.9171 |
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+ | 0.2978 | 5.8824 | 25 | 0.2819 | 0.8983 | 0.9092 | 0.8983 | 0.9024 |
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+ | 0.2978 | 6.8235 | 29 | 0.5332 | 0.9153 | 0.9230 | 0.9153 | 0.9010 |
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+ | 0.0283 | 8.0 | 34 | 0.5418 | 0.9153 | 0.9311 | 0.9153 | 0.9200 |
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+ | 0.0283 | 8.9412 | 38 | 0.6494 | 0.8983 | 0.9092 | 0.8983 | 0.8758 |
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+ | 0.0283 | 9.8824 | 42 | 0.5615 | 0.9153 | 0.9455 | 0.9153 | 0.9222 |
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+ | 0.0061 | 10.8235 | 46 | 0.8767 | 0.8983 | 0.8910 | 0.8983 | 0.8857 |
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+ | 0.0061 | 12.0 | 51 | 0.3859 | 0.9492 | 0.9619 | 0.9492 | 0.9520 |
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+ | 0.0061 | 12.9412 | 55 | 0.4550 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0061 | 13.8824 | 59 | 0.4314 | 0.9492 | 0.9477 | 0.9492 | 0.9479 |
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+ | 0.01 | 14.8235 | 63 | 0.4127 | 0.9492 | 0.9619 | 0.9492 | 0.9520 |
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+ | 0.01 | 16.0 | 68 | 0.3285 | 0.9492 | 0.9477 | 0.9492 | 0.9479 |
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+ | 0.01 | 16.9412 | 72 | 0.3180 | 0.9492 | 0.9477 | 0.9492 | 0.9479 |
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+ | 0.0076 | 17.8824 | 76 | 0.4482 | 0.9322 | 0.9293 | 0.9322 | 0.9286 |
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+ | 0.0076 | 18.8235 | 80 | 0.4437 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0076 | 20.0 | 85 | 0.4819 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0076 | 20.9412 | 89 | 0.5133 | 0.9322 | 0.9293 | 0.9322 | 0.9286 |
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+ | 0.0003 | 21.8824 | 93 | 0.4540 | 0.9492 | 0.9477 | 0.9492 | 0.9479 |
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+ | 0.0003 | 22.8235 | 97 | 0.3857 | 0.9153 | 0.9196 | 0.9153 | 0.9171 |
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+ | 0.0003 | 24.0 | 102 | 0.4077 | 0.8983 | 0.9092 | 0.8983 | 0.9024 |
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+ | 0.0028 | 24.9412 | 106 | 0.3956 | 0.9492 | 0.9477 | 0.9492 | 0.9479 |
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+ | 0.0028 | 25.8824 | 110 | 0.4671 | 0.9322 | 0.9293 | 0.9322 | 0.9286 |
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+ | 0.0028 | 26.8235 | 114 | 0.3811 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0028 | 28.0 | 119 | 0.3700 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0006 | 28.9412 | 123 | 0.4028 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0006 | 29.8824 | 127 | 0.6924 | 0.9153 | 0.9106 | 0.9153 | 0.9080 |
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+ | 0.0006 | 30.8235 | 131 | 0.6949 | 0.9153 | 0.9106 | 0.9153 | 0.9080 |
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+ | 0.0033 | 32.0 | 136 | 0.5889 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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+ | 0.0033 | 32.9412 | 140 | 0.5128 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0033 | 33.8824 | 144 | 0.4411 | 0.9492 | 0.9522 | 0.9492 | 0.9502 |
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+ | 0.0033 | 34.8235 | 148 | 0.4420 | 0.9492 | 0.9522 | 0.9492 | 0.9502 |
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+ | 0.0013 | 36.0 | 153 | 0.5616 | 0.9322 | 0.9322 | 0.9322 | 0.9322 |
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+ | 0.0013 | 36.9412 | 157 | 0.6365 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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+ | 0.0013 | 37.8824 | 161 | 0.6695 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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+ | 0.0001 | 38.8235 | 165 | 0.6846 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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+ | 0.0001 | 40.0 | 170 | 0.6930 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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+ | 0.0001 | 40.9412 | 174 | 0.6958 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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+ | 0.0001 | 41.8824 | 178 | 0.6967 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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+ | 0.0044 | 42.3529 | 180 | 0.6952 | 0.9153 | 0.9120 | 0.9153 | 0.9132 |
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  ### Framework versions
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