Model save

README.md

@@ -3,28 +3,12 @@ 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: finetuned-fake-food
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: indian_food_images
-      type: imagefolder
-      config: default
-      split: train
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.9523809523809523
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # finetuned-fake-food
 
-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 indian_food_images dataset.
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1446
-- Accuracy: 0.9524
+- Loss: 0.6577
+- Accuracy: 0.6096
 
 ## Model description
 
@@ -55,21 +39,59 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0002
-- train_batch_size: 8
+- train_batch_size: 2
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 20
+- num_epochs: 10
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
-|:-------------:|:-------:|:----:|:---------------:|:--------:|
-| 0.328         | 6.6667  | 100  | 0.3854          | 0.8571   |
-| 0.1729        | 13.3333 | 200  | 0.1446          | 0.9524   |
-| 0.0508        | 20.0    | 300  | 0.2076          | 0.9524   |
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.6977        | 0.2421 | 100  | 0.6805          | 0.5822   |
+| 0.6956        | 0.4843 | 200  | 0.6936          | 0.4178   |
+| 0.6795        | 0.7264 | 300  | 0.6734          | 0.6507   |
+| 0.7061        | 0.9685 | 400  | 0.6760          | 0.5822   |
+| 0.6941        | 1.2107 | 500  | 0.6746          | 0.5822   |
+| 0.6898        | 1.4528 | 600  | 0.6675          | 0.6027   |
+| 0.6956        | 1.6949 | 700  | 0.6846          | 0.5753   |
+| 0.6847        | 1.9370 | 800  | 0.6746          | 0.5822   |
+| 0.6949        | 2.1792 | 900  | 0.6780          | 0.5890   |
+| 0.703         | 2.4213 | 1000 | 0.6895          | 0.5753   |
+| 0.6851        | 2.6634 | 1100 | 0.6742          | 0.5822   |
+| 0.6878        | 2.9056 | 1200 | 0.6742          | 0.6301   |
+| 0.68          | 3.1477 | 1300 | 0.6713          | 0.5822   |
+| 0.6728        | 3.3898 | 1400 | 0.6838          | 0.5959   |
+| 0.698         | 3.6320 | 1500 | 0.6775          | 0.5822   |
+| 0.7033        | 3.8741 | 1600 | 0.6735          | 0.5822   |
+| 0.6973        | 4.1162 | 1700 | 0.6804          | 0.6233   |
+| 0.6822        | 4.3584 | 1800 | 0.6848          | 0.6027   |
+| 0.6896        | 4.6005 | 1900 | 0.6835          | 0.5411   |
+| 0.6772        | 4.8426 | 2000 | 0.6753          | 0.6096   |
+| 0.6843        | 5.0847 | 2100 | 0.6667          | 0.5890   |
+| 0.6898        | 5.3269 | 2200 | 0.6726          | 0.5822   |
+| 0.6868        | 5.5690 | 2300 | 0.6784          | 0.5616   |
+| 0.6636        | 5.8111 | 2400 | 0.6640          | 0.6301   |
+| 0.6833        | 6.0533 | 2500 | 0.6768          | 0.5137   |
+| 0.678         | 6.2954 | 2600 | 0.6652          | 0.6233   |
+| 0.6672        | 6.5375 | 2700 | 0.6735          | 0.5479   |
+| 0.6975        | 6.7797 | 2800 | 0.6687          | 0.5890   |
+| 0.6858        | 7.0218 | 2900 | 0.6672          | 0.6027   |
+| 0.6687        | 7.2639 | 3000 | 0.6648          | 0.5753   |
+| 0.6636        | 7.5061 | 3100 | 0.6674          | 0.5685   |
+| 0.6904        | 7.7482 | 3200 | 0.6752          | 0.5342   |
+| 0.6585        | 7.9903 | 3300 | 0.7023          | 0.5959   |
+| 0.6874        | 8.2324 | 3400 | 0.6615          | 0.5753   |
+| 0.6444        | 8.4746 | 3500 | 0.7721          | 0.5205   |
+| 0.6803        | 8.7167 | 3600 | 0.6809          | 0.5822   |
+| 0.6782        | 8.9588 | 3700 | 0.6638          | 0.5822   |
+| 0.6536        | 9.2010 | 3800 | 0.6607          | 0.6233   |
+| 0.6188        | 9.4431 | 3900 | 0.7090          | 0.5685   |
+| 0.7026        | 9.6852 | 4000 | 0.6574          | 0.6164   |
+| 0.7008        | 9.9274 | 4100 | 0.6577          | 0.6096   |
 
 
 ### Framework versions

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