Update README.md

README.md

@@ -1,80 +1,82 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: google/vit-base-patch16-224-in21k
-tags:
-- image-classification
-- generated_from_trainer
-datasets:
-- imagefolder
-metrics:
-- accuracy
-model-index:
-- name: vit-base-cat-emotions
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: custom dataset
-      type: imagefolder
-      config: default
-      split: validation
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.6352941176470588
----
-
-<!-- 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-cat-emotions
-
-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 custom dataset dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.0160
-- Accuracy: 0.6353
-
-## 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: 0.0002
-- train_batch_size: 16
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 10
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.3361        | 3.125 | 100  | 1.0125          | 0.6548   |
-| 0.0723        | 6.25  | 200  | 0.9043          | 0.7381   |
-| 0.0321        | 9.375 | 300  | 0.9268          | 0.7143   |
-
-
-### Framework versions
-
-- Transformers 4.44.1
-- Pytorch 2.2.2+cu118
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- image-classification
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-cat-emotions
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: custom dataset
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6352941176470588
+---
+
+<!-- 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-cat-emotions
+
+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 custom dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0160
+- Accuracy: 0.6353
+
+You can try out the model live [here](https://cat-emotion-classifier.streamlit.app/), and check out the [GitHub repository](https://github.com/semihdervis/cat-emotion-classifier) for more details.
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.3361        | 3.125 | 100  | 1.0125          | 0.6548   |
+| 0.0723        | 6.25  | 200  | 0.9043          | 0.7381   |
+| 0.0321        | 9.375 | 300  | 0.9268          | 0.7143   |
+
+
+### Framework versions
+
+- Transformers 4.44.1
+- Pytorch 2.2.2+cu118
+- Datasets 2.20.0
+- Tokenizers 0.19.1