End of training

README.md

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+---
+license: apache-2.0
+base_model: microsoft/swinv2-base-patch4-window8-256
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- recall
+- f1
+- precision
+model-index:
+- name: swinv2-base-patch4-window8-256-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: 0.8407833120476799
+    - name: Recall
+      type: recall
+      value: 0.8407833120476799
+    - name: F1
+      type: f1
+      value: 0.8382298834449193
+    - name: Precision
+      type: precision
+      value: 0.8403613762272836
+---
+
+<!-- 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. -->
+
+# swinv2-base-patch4-window8-256-finetuned-ind-17-imbalanced-aadhaarmask
+
+This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3672
+- Accuracy: 0.8408
+- Recall: 0.8408
+- F1: 0.8382
+- Precision: 0.8404
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.6524        | 0.9974 | 293  | 0.5989          | 0.7986   | 0.7986 | 0.7886 | 0.7959    |
+| 0.5004        | 1.9983 | 587  | 0.4830          | 0.8110   | 0.8110 | 0.8078 | 0.8190    |
+| 0.3912        | 2.9991 | 881  | 0.4254          | 0.8199   | 0.8199 | 0.8162 | 0.8196    |
+| 0.4007        | 4.0    | 1175 | 0.4324          | 0.8301   | 0.8301 | 0.8251 | 0.8302    |
+| 0.2694        | 4.9974 | 1468 | 0.4215          | 0.8272   | 0.8272 | 0.8218 | 0.8301    |
+| 0.3865        | 5.9983 | 1762 | 0.3620          | 0.8459   | 0.8459 | 0.8438 | 0.8471    |
+| 0.2748        | 6.9991 | 2056 | 0.3733          | 0.8395   | 0.8395 | 0.8354 | 0.8510    |
+| 0.3471        | 8.0    | 2350 | 0.3594          | 0.8370   | 0.8370 | 0.8364 | 0.8434    |
+| 0.3361        | 8.9974 | 2643 | 0.3632          | 0.8404   | 0.8404 | 0.8386 | 0.8414    |
+| 0.2399        | 9.9745 | 2930 | 0.3436          | 0.8455   | 0.8455 | 0.8446 | 0.8469    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0a0+81ea7a4
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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