End of training

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swin-base-patch4-window7-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- recall
+- f1
+- precision
+model-index:
+- name: swin-base-patch4-window7-224-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: 0.855683269476373
+    - name: Recall
+      type: recall
+      value: 0.855683269476373
+    - name: F1
+      type: f1
+      value: 0.8542203503644927
+    - name: Precision
+      type: precision
+      value: 0.8559779206156822
+---
+
+<!-- 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. -->
+
+# swin-base-patch4-window7-224-finetuned-ind-17-imbalanced-aadhaarmask
+
+This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3209
+- Accuracy: 0.8557
+- Recall: 0.8557
+- F1: 0.8542
+- Precision: 0.8560
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.5155        | 0.9974 | 293  | 0.5710          | 0.7935   | 0.7935 | 0.7821 | 0.7895    |
+| 0.4245        | 1.9983 | 587  | 0.4729          | 0.8238   | 0.8238 | 0.8187 | 0.8266    |
+| 0.4183        | 2.9991 | 881  | 0.4145          | 0.8408   | 0.8408 | 0.8309 | 0.8350    |
+| 0.4088        | 4.0    | 1175 | 0.3901          | 0.8425   | 0.8425 | 0.8375 | 0.8501    |
+| 0.3489        | 4.9974 | 1468 | 0.3703          | 0.8463   | 0.8463 | 0.8446 | 0.8518    |
+| 0.3115        | 5.9983 | 1762 | 0.3500          | 0.8540   | 0.8540 | 0.8525 | 0.8605    |
+| 0.3087        | 6.9991 | 2056 | 0.3338          | 0.8519   | 0.8519 | 0.8494 | 0.8582    |
+| 0.2372        | 8.0    | 2350 | 0.3181          | 0.8548   | 0.8548 | 0.8543 | 0.8587    |
+| 0.2816        | 8.9974 | 2643 | 0.3167          | 0.8536   | 0.8536 | 0.8530 | 0.8561    |
+| 0.2378        | 9.9745 | 2930 | 0.3063          | 0.8702   | 0.8702 | 0.8686 | 0.8709    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0a0+81ea7a4
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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