End of training

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: msi-nat-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: 0.8460220784164446
+    - name: F1
+      type: f1
+      value: 0.8017318846499469
+    - name: Precision
+      type: precision
+      value: 0.8296559303406882
+    - name: Recall
+      type: recall
+      value: 0.7756263336758081
+---
+
+<!-- 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-nat-mini
+
+This model was trained from scratch on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3451
+- Accuracy: 0.8460
+- F1: 0.8017
+- Precision: 0.8297
+- Recall: 0.7756
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.5705        | 1.0   | 1970  | 0.5230          | 0.7410   | 0.6588 | 0.6988    | 0.6232 |
+| 0.4805        | 2.0   | 3941  | 0.4447          | 0.7924   | 0.7298 | 0.7640    | 0.6986 |
+| 0.4521        | 3.0   | 5911  | 0.4090          | 0.8107   | 0.7518 | 0.7936    | 0.7141 |
+| 0.4343        | 4.0   | 7882  | 0.3878          | 0.8239   | 0.7768 | 0.7907    | 0.7634 |
+| 0.4003        | 5.0   | 9852  | 0.3720          | 0.8328   | 0.7850 | 0.8113    | 0.7604 |
+| 0.3887        | 6.0   | 11823 | 0.3620          | 0.8376   | 0.7875 | 0.8295    | 0.7496 |
+| 0.3709        | 7.0   | 13793 | 0.3506          | 0.8435   | 0.7977 | 0.8286    | 0.7690 |
+| 0.3686        | 8.0   | 15764 | 0.3473          | 0.8461   | 0.8025 | 0.8271    | 0.7793 |
+| 0.3819        | 9.0   | 17734 | 0.3422          | 0.8476   | 0.8052 | 0.8270    | 0.7845 |
+| 0.3838        | 10.0  | 19700 | 0.3451          | 0.8460   | 0.8017 | 0.8297    | 0.7756 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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