Model save

README.md

@@ -0,0 +1,88 @@
+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- f1
+- accuracy
+model-index:
+- name: vet-sm
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.47
+    - name: Accuracy
+      type: accuracy
+      value: 0.31
+---
+
+<!-- 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. -->
+
+# vet-sm
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2940
+- F1: 0.47
+- Roc Auc: 0.66
+- Accuracy: 0.31
+
+## 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: 16
+- 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1   | Roc Auc | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|:--------:|
+| 0.4074        | 0.99  | 67   | 0.3991          | 0.0  | 0.5     | 0.0      |
+| 0.3597        | 2.0   | 135  | 0.3546          | 0.01 | 0.5     | 0.01     |
+| 0.3191        | 2.99  | 202  | 0.3307          | 0.18 | 0.55    | 0.1      |
+| 0.2902        | 4.0   | 270  | 0.3034          | 0.39 | 0.62    | 0.25     |
+| 0.2749        | 4.96  | 335  | 0.2940          | 0.47 | 0.66    | 0.31     |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.0
+- Datasets 2.16.1
+- Tokenizers 0.15.1