End of training

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: msi-dinat-mini
+  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: 0.6307660050321499
+    - name: F1
+      type: f1
+      value: 0.45316219853017287
+    - name: Precision
+      type: precision
+      value: 0.6338497176777182
+    - name: Recall
+      type: recall
+      value: 0.3526379379782521
+---
+
+<!-- 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-dinat-mini
+
+This model was trained from scratch on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8735
+- Accuracy: 0.6308
+- F1: 0.4532
+- Precision: 0.6338
+- Recall: 0.3526
+
+## 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: 1e-06
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.5414        | 1.0   | 2015  | 0.7584          | 0.5874   | 0.3960 | 0.5427    | 0.3117 |
+| 0.4715        | 2.0   | 4031  | 0.7695          | 0.6208   | 0.4593 | 0.6021    | 0.3712 |
+| 0.4159        | 3.0   | 6047  | 0.7922          | 0.6230   | 0.4637 | 0.6056    | 0.3757 |
+| 0.3774        | 4.0   | 8063  | 0.8166          | 0.6286   | 0.4589 | 0.6235    | 0.3630 |
+| 0.3635        | 5.0   | 10078 | 0.8123          | 0.6349   | 0.4889 | 0.6225    | 0.4026 |
+| 0.3471        | 6.0   | 12094 | 0.8481          | 0.6265   | 0.4575 | 0.6186    | 0.3630 |
+| 0.3616        | 7.0   | 14110 | 0.8605          | 0.6284   | 0.4514 | 0.6279    | 0.3524 |
+| 0.3517        | 8.0   | 16126 | 0.8661          | 0.6329   | 0.4600 | 0.6356    | 0.3604 |
+| 0.3476        | 9.0   | 18141 | 0.8631          | 0.6330   | 0.4619 | 0.6346    | 0.3631 |
+| 0.3469        | 10.0  | 20150 | 0.8735          | 0.6308   | 0.4532 | 0.6338    | 0.3526 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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