End of training

README.md

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+---
+license: apache-2.0
+base_model: microsoft/beit-large-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: smids_10x_beit_large_sgd_00001_fold4
+  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: 0.6483333333333333
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# smids_10x_beit_large_sgd_00001_fold4
+
+This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7900
+- Accuracy: 0.6483
+
+## 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-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.2211        | 1.0   | 750   | 1.1931          | 0.3533   |
+| 1.1323        | 2.0   | 1500  | 1.1612          | 0.365    |
+| 1.184         | 3.0   | 2250  | 1.1336          | 0.37     |
+| 1.1136        | 4.0   | 3000  | 1.1091          | 0.3817   |
+| 0.9758        | 5.0   | 3750  | 1.0870          | 0.385    |
+| 1.0842        | 6.0   | 4500  | 1.0669          | 0.3917   |
+| 1.0165        | 7.0   | 5250  | 1.0484          | 0.4117   |
+| 1.0062        | 8.0   | 6000  | 1.0310          | 0.43     |
+| 1.0015        | 9.0   | 6750  | 1.0148          | 0.4367   |
+| 0.9415        | 10.0  | 7500  | 0.9997          | 0.45     |
+| 0.9588        | 11.0  | 8250  | 0.9856          | 0.4533   |
+| 0.9674        | 12.0  | 9000  | 0.9724          | 0.47     |
+| 0.9046        | 13.0  | 9750  | 0.9600          | 0.4733   |
+| 0.9542        | 14.0  | 10500 | 0.9483          | 0.4867   |
+| 0.8663        | 15.0  | 11250 | 0.9372          | 0.5      |
+| 0.8717        | 16.0  | 12000 | 0.9268          | 0.51     |
+| 0.7922        | 17.0  | 12750 | 0.9171          | 0.525    |
+| 0.8562        | 18.0  | 13500 | 0.9078          | 0.535    |
+| 0.9212        | 19.0  | 14250 | 0.8991          | 0.5433   |
+| 0.8823        | 20.0  | 15000 | 0.8907          | 0.5567   |
+| 0.8498        | 21.0  | 15750 | 0.8828          | 0.565    |
+| 0.8335        | 22.0  | 16500 | 0.8754          | 0.575    |
+| 0.8369        | 23.0  | 17250 | 0.8683          | 0.5867   |
+| 0.8886        | 24.0  | 18000 | 0.8617          | 0.5917   |
+| 0.8131        | 25.0  | 18750 | 0.8555          | 0.6      |
+| 0.8107        | 26.0  | 19500 | 0.8497          | 0.605    |
+| 0.7489        | 27.0  | 20250 | 0.8442          | 0.61     |
+| 0.8154        | 28.0  | 21000 | 0.8390          | 0.6167   |
+| 0.7935        | 29.0  | 21750 | 0.8341          | 0.62     |
+| 0.7606        | 30.0  | 22500 | 0.8296          | 0.6267   |
+| 0.7688        | 31.0  | 23250 | 0.8253          | 0.6283   |
+| 0.755         | 32.0  | 24000 | 0.8214          | 0.63     |
+| 0.8046        | 33.0  | 24750 | 0.8176          | 0.63     |
+| 0.8193        | 34.0  | 25500 | 0.8142          | 0.6317   |
+| 0.7668        | 35.0  | 26250 | 0.8110          | 0.635    |
+| 0.7573        | 36.0  | 27000 | 0.8080          | 0.6367   |
+| 0.7928        | 37.0  | 27750 | 0.8053          | 0.6417   |
+| 0.792         | 38.0  | 28500 | 0.8028          | 0.6417   |
+| 0.7917        | 39.0  | 29250 | 0.8007          | 0.645    |
+| 0.7521        | 40.0  | 30000 | 0.7987          | 0.645    |
+| 0.777         | 41.0  | 30750 | 0.7969          | 0.6483   |
+| 0.7956        | 42.0  | 31500 | 0.7954          | 0.6483   |
+| 0.8067        | 43.0  | 32250 | 0.7940          | 0.65     |
+| 0.7335        | 44.0  | 33000 | 0.7929          | 0.65     |
+| 0.7708        | 45.0  | 33750 | 0.7920          | 0.6483   |
+| 0.74          | 46.0  | 34500 | 0.7912          | 0.6483   |
+| 0.7222        | 47.0  | 35250 | 0.7906          | 0.6483   |
+| 0.7572        | 48.0  | 36000 | 0.7902          | 0.6483   |
+| 0.7909        | 49.0  | 36750 | 0.7900          | 0.6483   |
+| 0.7055        | 50.0  | 37500 | 0.7900          | 0.6483   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2

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