Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: google-vit-base-patch16-224-cartoon-emotion-detection
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8807339449541285
+    - name: Precision
+      type: precision
+      value: 0.8768597487153273
+    - name: Recall
+      type: recall
+      value: 0.8807339449541285
+    - name: F1
+      type: f1
+      value: 0.8782945902988435
+---
+
+<!-- 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. -->
+
+# google-vit-base-patch16-224-cartoon-emotion-detection
+
+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: 0.3706
+- Accuracy: 0.8807
+- Precision: 0.8769
+- Recall: 0.8807
+- F1: 0.8783
+
+## 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: 0.00012
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 256
+- 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 | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 0.97  | 8    | 0.9902          | 0.5596   | 0.5506    | 0.5596 | 0.5360 |
+| 1.242         | 1.97  | 16   | 0.5157          | 0.8165   | 0.8195    | 0.8165 | 0.8132 |
+| 0.4438        | 2.97  | 24   | 0.3871          | 0.8440   | 0.8516    | 0.8440 | 0.8446 |
+| 0.1768        | 3.97  | 32   | 0.3531          | 0.8624   | 0.8653    | 0.8624 | 0.8585 |
+| 0.0661        | 4.97  | 40   | 0.3780          | 0.8716   | 0.8693    | 0.8716 | 0.8674 |
+| 0.0661        | 5.97  | 48   | 0.3747          | 0.8624   | 0.8649    | 0.8624 | 0.8632 |
+| 0.0375        | 6.97  | 56   | 0.3760          | 0.8991   | 0.8961    | 0.8991 | 0.8971 |
+| 0.0362        | 7.97  | 64   | 0.4092          | 0.8716   | 0.8684    | 0.8716 | 0.8681 |
+| 0.0322        | 8.97  | 72   | 0.3499          | 0.8899   | 0.8880    | 0.8899 | 0.8888 |
+| 0.029         | 9.97  | 80   | 0.3706          | 0.8807   | 0.8769    | 0.8807 | 0.8783 |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.1+cu117
+- Datasets 2.8.0
+- Tokenizers 0.11.0