End of training

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: msi-vit-small
+  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: 0.599979032708974
+    - name: F1
+      type: f1
+      value: 0.2863021385373153
+    - name: Precision
+      type: precision
+      value: 0.6335540838852097
+    - name: Recall
+      type: recall
+      value: 0.18493757551349174
+---
+
+<!-- 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
+
+This model was trained from scratch on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5796
+- Accuracy: 0.6000
+- F1: 0.2863
+- Precision: 0.6336
+- Recall: 0.1849
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.3142        | 1.0   | 1008  | 0.8965          | 0.6329   | 0.5060 | 0.6079    | 0.4333 |
+| 0.2063        | 2.0   | 2016  | 1.5189          | 0.6062   | 0.3005 | 0.6550    | 0.1950 |
+| 0.19          | 3.0   | 3024  | 1.4818          | 0.6270   | 0.3399 | 0.7318    | 0.2213 |
+| 0.1718        | 4.0   | 4032  | 1.2353          | 0.6046   | 0.4096 | 0.5816    | 0.3161 |
+| 0.161         | 5.0   | 5040  | 1.5953          | 0.6342   | 0.3508 | 0.7623    | 0.2278 |
+| 0.1805        | 6.0   | 6048  | 1.0789          | 0.6552   | 0.4647 | 0.7119    | 0.3449 |
+| 0.1619        | 7.0   | 7056  | 1.2646          | 0.5479   | 0.2591 | 0.4484    | 0.1822 |
+| 0.1655        | 8.0   | 8064  | 1.7155          | 0.5910   | 0.2654 | 0.6011    | 0.1703 |
+| 0.17          | 9.0   | 9072  | 2.1142          | 0.5797   | 0.1729 | 0.5913    | 0.1012 |
+| 0.1703        | 10.0  | 10080 | 1.5796          | 0.6000   | 0.2863 | 0.6336    | 0.1849 |
+
+
+### Framework versions
+
+- Transformers 4.36.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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