End of training

README.md

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+---
+license: apache-2.0
+base_model: google/vit-large-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- recall
+- f1
+- precision
+model-index:
+- name: vit-large-patch16-224-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: 0.8420604512558536
+    - name: Recall
+      type: recall
+      value: 0.8420604512558536
+    - name: F1
+      type: f1
+      value: 0.840458775689156
+    - name: Precision
+      type: precision
+      value: 0.8450034699086092
+---
+
+<!-- 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. -->
+
+# vit-large-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask
+
+This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3294
+- Accuracy: 0.8421
+- Recall: 0.8421
+- F1: 0.8405
+- Precision: 0.8450
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.5269        | 0.9974 | 293  | 0.5393          | 0.8029   | 0.8029 | 0.7943 | 0.7941    |
+| 0.4275        | 1.9983 | 587  | 0.4630          | 0.8182   | 0.8182 | 0.8103 | 0.8255    |
+| 0.4681        | 2.9991 | 881  | 0.4346          | 0.8408   | 0.8408 | 0.8358 | 0.8557    |
+| 0.3721        | 4.0    | 1175 | 0.3631          | 0.8450   | 0.8450 | 0.8417 | 0.8541    |
+| 0.4054        | 4.9974 | 1468 | 0.3536          | 0.8455   | 0.8455 | 0.8445 | 0.8491    |
+| 0.2519        | 5.9983 | 1762 | 0.3747          | 0.8421   | 0.8421 | 0.8391 | 0.8549    |
+| 0.2923        | 6.9991 | 2056 | 0.3664          | 0.8395   | 0.8395 | 0.8402 | 0.8467    |
+| 0.2288        | 8.0    | 2350 | 0.3496          | 0.8382   | 0.8382 | 0.8377 | 0.8442    |
+| 0.1642        | 8.9974 | 2643 | 0.3455          | 0.8463   | 0.8463 | 0.8444 | 0.8468    |
+| 0.1783        | 9.9745 | 2930 | 0.3468          | 0.8476   | 0.8476 | 0.8463 | 0.8490    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0a0+81ea7a4
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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