End of training

README.md

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+---
+license: apache-2.0
+base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- recall
+- f1
+- precision
+model-index:
+- name: beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8450404427415922
+    - name: Recall
+      type: recall
+      value: 0.8450404427415922
+    - name: F1
+      type: f1
+      value: 0.8442233792705293
+    - name: Precision
+      type: precision
+      value: 0.8494143266059094
+---
+
+<!-- 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. -->
+
+# beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask
+
+This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3480
+- Accuracy: 0.8450
+- Recall: 0.8450
+- F1: 0.8442
+- Precision: 0.8494
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.5859        | 0.9974 | 293  | 0.6117          | 0.8114   | 0.8114 | 0.7891 | 0.8139    |
+| 0.5281        | 1.9983 | 587  | 0.4362          | 0.8442   | 0.8442 | 0.8375 | 0.8484    |
+| 0.4214        | 2.9991 | 881  | 0.4228          | 0.8438   | 0.8438 | 0.8392 | 0.8529    |
+| 0.4221        | 4.0    | 1175 | 0.4121          | 0.8382   | 0.8382 | 0.8331 | 0.8495    |
+| 0.4127        | 4.9974 | 1468 | 0.3692          | 0.8476   | 0.8476 | 0.8454 | 0.8511    |
+| 0.3122        | 5.9983 | 1762 | 0.3741          | 0.8408   | 0.8408 | 0.8394 | 0.8462    |
+| 0.3079        | 6.9991 | 2056 | 0.3628          | 0.8429   | 0.8429 | 0.8403 | 0.8445    |
+| 0.2851        | 8.0    | 2350 | 0.3635          | 0.8412   | 0.8412 | 0.8389 | 0.8412    |
+| 0.297         | 8.9974 | 2643 | 0.3407          | 0.8510   | 0.8510 | 0.8497 | 0.8545    |
+| 0.2109        | 9.9745 | 2930 | 0.3566          | 0.8421   | 0.8421 | 0.8406 | 0.8418    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0a0+81ea7a4
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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