update model card README.md

README.md

@@ -1,23 +1,38 @@
 ---
 license: apache-2.0
 tags:
-- generated_from_keras_callback
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
 model-index:
-- name: rwang5688/vit-base-patch16-224-finetuned-eurosat
-  results: []
+- name: vit-base-patch16-224-finetuned-eurosat
+  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: 0.9855555555555555
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
+<!-- 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. -->
 
-# rwang5688/vit-base-patch16-224-finetuned-eurosat
+# vit-base-patch16-224-finetuned-eurosat
 
-This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
+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:
-- Train Loss: 0.3145
-- Validation Loss: 0.0427
-- Validation Accuracy: 0.9870
-- Epoch: 2
+- Loss: 0.0469
+- Accuracy: 0.9856
 
 ## Model description
 
@@ -36,21 +51,29 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
-- training_precision: float32
+- learning_rate: 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: 0.1
+- num_epochs: 3
 
 ### Training results
 
-| Train Loss | Validation Loss | Validation Accuracy | Epoch |
-|:----------:|:---------------:|:-------------------:|:-----:|
-| 0.4985     | 0.1121          | 0.9641              | 0     |
-| 0.3527     | 0.0535          | 0.9826              | 1     |
-| 0.3145     | 0.0427          | 0.9870              | 2     |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.1491        | 1.0   | 190  | 0.0890          | 0.9715   |
+| 0.1021        | 2.0   | 380  | 0.0578          | 0.9811   |
+| 0.0694        | 3.0   | 570  | 0.0469          | 0.9856   |
 
 
 ### Framework versions
 
 - Transformers 4.21.1
-- TensorFlow 2.9.1
+- Pytorch 1.12.1+cu102
 - Datasets 2.4.0
 - Tokenizers 0.12.1