Update README.md

README.md

@@ -1,87 +1,87 @@
----
-license: apache-2.0
-base_model: google/vit-base-patch16-224
-tags:
-- generated_from_trainer
-datasets:
-- imagefolder
-metrics:
-- accuracy
-model-index:
-- name: vit-base-patch16-224-U8-10b
-  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: 0.8627450980392157
----
-
-<!-- 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. -->
-
-# vit-base-patch16-224-U8-10b
-
-This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.5349
-- Accuracy: 0.8627
-
-## 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: 5.5e-05
-- 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: 0.05
-- num_epochs: 10
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.2473        | 1.0   | 20   | 1.1671          | 0.5882   |
-| 0.955         | 2.0   | 40   | 0.9392          | 0.6471   |
-| 0.735         | 3.0   | 60   | 0.7247          | 0.6863   |
-| 0.5341        | 4.0   | 80   | 0.5977          | 0.8235   |
-| 0.3864        | 5.0   | 100  | 0.6556          | 0.7451   |
-| 0.2837        | 6.0   | 120  | 0.6781          | 0.7255   |
-| 0.2332        | 7.0   | 140  | 0.5419          | 0.8431   |
-| 0.1974        | 8.0   | 160  | 0.5349          | 0.8627   |
-| 0.1857        | 9.0   | 180  | 0.5606          | 0.8235   |
-| 0.1907        | 10.0  | 200  | 0.4875          | 0.8431   |
-
-
-### Framework versions
-
-- Transformers 4.36.2
-- Pytorch 2.1.2+cu118
-- Datasets 2.16.1
-- Tokenizers 0.15.0
+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-patch16-224-U8-10b
+  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: 0.8627450980392157
+---
+
+<!-- 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. -->
+
+# vit-base-patch16-224-U8-10b
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset "dmae-ve-U8".
+It achieves the following results on the evaluation set:
+- Loss: 0.5349
+- Accuracy: 0.8627
+
+## 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: 5.5e-05
+- 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: 0.05
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.2473        | 1.0   | 20   | 1.1671          | 0.5882   |
+| 0.955         | 2.0   | 40   | 0.9392          | 0.6471   |
+| 0.735         | 3.0   | 60   | 0.7247          | 0.6863   |
+| 0.5341        | 4.0   | 80   | 0.5977          | 0.8235   |
+| 0.3864        | 5.0   | 100  | 0.6556          | 0.7451   |
+| 0.2837        | 6.0   | 120  | 0.6781          | 0.7255   |
+| 0.2332        | 7.0   | 140  | 0.5419          | 0.8431   |
+| 0.1974        | 8.0   | 160  | 0.5349          | 0.8627   |
+| 0.1857        | 9.0   | 180  | 0.5606          | 0.8235   |
+| 0.1907        | 10.0  | 200  | 0.4875          | 0.8431   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu118
+- Datasets 2.16.1
+- Tokenizers 0.15.0