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--- |
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license: apache-2.0 |
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base_model: microsoft/beit-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: beit-base-patch16-224-hasta-75-fold3 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9166666666666666 |
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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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# beit-base-patch16-224-hasta-75-fold3 |
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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 the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5330 |
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- Accuracy: 0.9167 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 1 | 1.2661 | 0.1667 | |
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| No log | 2.0 | 2 | 0.9215 | 0.5 | |
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| No log | 3.0 | 3 | 0.5330 | 0.9167 | |
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| No log | 4.0 | 4 | 0.4361 | 0.9167 | |
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| No log | 5.0 | 5 | 0.4724 | 0.9167 | |
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| No log | 6.0 | 6 | 0.4424 | 0.9167 | |
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| No log | 7.0 | 7 | 0.3788 | 0.9167 | |
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| No log | 8.0 | 8 | 0.4228 | 0.9167 | |
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| No log | 9.0 | 9 | 0.4592 | 0.9167 | |
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| 0.3974 | 10.0 | 10 | 0.3966 | 0.9167 | |
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| 0.3974 | 11.0 | 11 | 0.3517 | 0.9167 | |
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| 0.3974 | 12.0 | 12 | 0.3481 | 0.9167 | |
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| 0.3974 | 13.0 | 13 | 0.3315 | 0.9167 | |
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| 0.3974 | 14.0 | 14 | 0.3353 | 0.9167 | |
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| 0.3974 | 15.0 | 15 | 0.3784 | 0.9167 | |
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| 0.3974 | 16.0 | 16 | 0.4232 | 0.9167 | |
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| 0.3974 | 17.0 | 17 | 0.4654 | 0.9167 | |
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| 0.3974 | 18.0 | 18 | 0.4091 | 0.9167 | |
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| 0.3974 | 19.0 | 19 | 0.4149 | 0.9167 | |
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| 0.1925 | 20.0 | 20 | 0.4202 | 0.9167 | |
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| 0.1925 | 21.0 | 21 | 0.4502 | 0.9167 | |
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| 0.1925 | 22.0 | 22 | 0.4371 | 0.9167 | |
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| 0.1925 | 23.0 | 23 | 0.4291 | 0.9167 | |
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| 0.1925 | 24.0 | 24 | 0.4265 | 0.8333 | |
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| 0.1925 | 25.0 | 25 | 0.4414 | 0.8333 | |
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| 0.1925 | 26.0 | 26 | 0.3957 | 0.8333 | |
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| 0.1925 | 27.0 | 27 | 0.3567 | 0.9167 | |
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| 0.1925 | 28.0 | 28 | 0.3406 | 0.9167 | |
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| 0.1925 | 29.0 | 29 | 0.3101 | 0.9167 | |
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| 0.1407 | 30.0 | 30 | 0.2956 | 0.9167 | |
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| 0.1407 | 31.0 | 31 | 0.3548 | 0.9167 | |
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| 0.1407 | 32.0 | 32 | 0.3067 | 0.9167 | |
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| 0.1407 | 33.0 | 33 | 0.2485 | 0.9167 | |
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| 0.1407 | 34.0 | 34 | 0.2818 | 0.9167 | |
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| 0.1407 | 35.0 | 35 | 0.3197 | 0.9167 | |
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| 0.1407 | 36.0 | 36 | 0.3401 | 0.9167 | |
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| 0.1407 | 37.0 | 37 | 0.3282 | 0.9167 | |
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| 0.1407 | 38.0 | 38 | 0.3078 | 0.9167 | |
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| 0.1407 | 39.0 | 39 | 0.2906 | 0.9167 | |
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| 0.1204 | 40.0 | 40 | 0.2875 | 0.9167 | |
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| 0.1204 | 41.0 | 41 | 0.3188 | 0.9167 | |
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| 0.1204 | 42.0 | 42 | 0.3449 | 0.9167 | |
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| 0.1204 | 43.0 | 43 | 0.3520 | 0.9167 | |
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| 0.1204 | 44.0 | 44 | 0.3401 | 0.9167 | |
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| 0.1204 | 45.0 | 45 | 0.3029 | 0.9167 | |
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| 0.1204 | 46.0 | 46 | 0.2584 | 0.9167 | |
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| 0.1204 | 47.0 | 47 | 0.2358 | 0.9167 | |
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| 0.1204 | 48.0 | 48 | 0.2265 | 0.9167 | |
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| 0.1204 | 49.0 | 49 | 0.2144 | 0.9167 | |
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| 0.0691 | 50.0 | 50 | 0.1622 | 0.9167 | |
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| 0.0691 | 51.0 | 51 | 0.1094 | 0.9167 | |
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| 0.0691 | 52.0 | 52 | 0.1955 | 0.9167 | |
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| 0.0691 | 53.0 | 53 | 0.3863 | 0.9167 | |
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| 0.0691 | 54.0 | 54 | 0.4803 | 0.9167 | |
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| 0.0691 | 55.0 | 55 | 0.5175 | 0.9167 | |
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| 0.0691 | 56.0 | 56 | 0.4899 | 0.9167 | |
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| 0.0691 | 57.0 | 57 | 0.4092 | 0.9167 | |
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| 0.0691 | 58.0 | 58 | 0.3755 | 0.9167 | |
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| 0.0691 | 59.0 | 59 | 0.3642 | 0.9167 | |
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| 0.062 | 60.0 | 60 | 0.4002 | 0.9167 | |
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| 0.062 | 61.0 | 61 | 0.4086 | 0.9167 | |
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| 0.062 | 62.0 | 62 | 0.4066 | 0.9167 | |
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| 0.062 | 63.0 | 63 | 0.3781 | 0.9167 | |
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| 0.062 | 64.0 | 64 | 0.3259 | 0.9167 | |
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| 0.062 | 65.0 | 65 | 0.2518 | 0.9167 | |
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| 0.062 | 66.0 | 66 | 0.2186 | 0.9167 | |
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| 0.062 | 67.0 | 67 | 0.2601 | 0.9167 | |
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| 0.062 | 68.0 | 68 | 0.2965 | 0.9167 | |
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| 0.062 | 69.0 | 69 | 0.3699 | 0.9167 | |
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| 0.0313 | 70.0 | 70 | 0.4417 | 0.9167 | |
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| 0.0313 | 71.0 | 71 | 0.5105 | 0.9167 | |
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| 0.0313 | 72.0 | 72 | 0.5439 | 0.9167 | |
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| 0.0313 | 73.0 | 73 | 0.5557 | 0.9167 | |
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| 0.0313 | 74.0 | 74 | 0.5514 | 0.9167 | |
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| 0.0313 | 75.0 | 75 | 0.5486 | 0.9167 | |
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| 0.0313 | 76.0 | 76 | 0.5317 | 0.9167 | |
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| 0.0313 | 77.0 | 77 | 0.4996 | 0.9167 | |
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| 0.0313 | 78.0 | 78 | 0.4638 | 0.9167 | |
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| 0.0313 | 79.0 | 79 | 0.4196 | 0.9167 | |
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| 0.0359 | 80.0 | 80 | 0.3639 | 0.9167 | |
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| 0.0359 | 81.0 | 81 | 0.3530 | 0.9167 | |
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| 0.0359 | 82.0 | 82 | 0.3918 | 0.9167 | |
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| 0.0359 | 83.0 | 83 | 0.4290 | 0.9167 | |
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| 0.0359 | 84.0 | 84 | 0.4569 | 0.9167 | |
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| 0.0359 | 85.0 | 85 | 0.4849 | 0.9167 | |
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| 0.0359 | 86.0 | 86 | 0.5136 | 0.9167 | |
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| 0.0359 | 87.0 | 87 | 0.5406 | 0.9167 | |
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| 0.0359 | 88.0 | 88 | 0.5586 | 0.9167 | |
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| 0.0359 | 89.0 | 89 | 0.5745 | 0.9167 | |
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| 0.0338 | 90.0 | 90 | 0.5878 | 0.9167 | |
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| 0.0338 | 91.0 | 91 | 0.5981 | 0.9167 | |
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| 0.0338 | 92.0 | 92 | 0.6071 | 0.9167 | |
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| 0.0338 | 93.0 | 93 | 0.6133 | 0.9167 | |
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| 0.0338 | 94.0 | 94 | 0.6139 | 0.9167 | |
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| 0.0338 | 95.0 | 95 | 0.6106 | 0.9167 | |
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| 0.0338 | 96.0 | 96 | 0.6059 | 0.9167 | |
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| 0.0338 | 97.0 | 97 | 0.6019 | 0.9167 | |
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| 0.0338 | 98.0 | 98 | 0.5981 | 0.9167 | |
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| 0.0338 | 99.0 | 99 | 0.5942 | 0.9167 | |
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| 0.0302 | 100.0 | 100 | 0.5923 | 0.9167 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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