End of training

README.md

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+---
+license: apache-2.0
+base_model: WinKawaks/vit-small-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: msi-vit-small-1218-2
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6164383561643836
+    - name: F1
+      type: f1
+      value: 0.3276157804459692
+    - name: Precision
+      type: precision
+      value: 0.6840624200562804
+    - name: Recall
+      type: recall
+      value: 0.2153846153846154
+---
+
+<!-- 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. -->
+
+# msi-vit-small-1218-2
+
+This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3372
+- Accuracy: 0.6164
+- F1: 0.3276
+- Precision: 0.6841
+- Recall: 0.2154
+
+## 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-06
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- 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 | F1     | Precision | Recall |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.4367        | 1.0   | 1008  | 0.6603          | 0.6572   | 0.5313 | 0.6530    | 0.4478 |
+| 0.2161        | 2.0   | 2016  | 0.8021          | 0.6329   | 0.4989 | 0.6118    | 0.4211 |
+| 0.169         | 3.0   | 3024  | 1.4062          | 0.6010   | 0.2653 | 0.6592    | 0.1661 |
+| 0.1543        | 4.0   | 4032  | 1.1498          | 0.6259   | 0.3670 | 0.6903    | 0.2499 |
+| 0.1534        | 5.0   | 5040  | 1.5067          | 0.6208   | 0.3519 | 0.6808    | 0.2373 |
+| 0.1596        | 6.0   | 6048  | 0.8837          | 0.6504   | 0.6505 | 0.5744    | 0.7498 |
+| 0.1504        | 7.0   | 7056  | 1.0030          | 0.6302   | 0.4192 | 0.6580    | 0.3075 |
+| 0.1795        | 8.0   | 8064  | 1.3908          | 0.5953   | 0.2950 | 0.6041    | 0.1952 |
+| 0.1636        | 9.0   | 9072  | 1.1040          | 0.6290   | 0.4619 | 0.6230    | 0.3671 |
+| 0.1629        | 10.0  | 10080 | 1.3372          | 0.6164   | 0.3276 | 0.6841    | 0.2154 |
+
+
+### Framework versions
+
+- Transformers 4.36.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.15.0
+- Tokenizers 0.15.0

model.safetensors

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