End of training

README.md

@@ -4,7 +4,7 @@ base_model: google/vit-base-patch16-224-in21k
 tags:
 - generated_from_trainer
 datasets:
-- food101
+- imagefolder
 metrics:
 - accuracy
 model-index:
@@ -14,15 +14,15 @@ model-index:
       name: Image Classification
       type: image-classification
     dataset:
-      name: food101
-      type: food101
+      name: imagefolder
+      type: imagefolder
       config: default
-      split: train[:5000]
+      split: train
       args: default
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.884
+      value: 0.15625
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # image_classification
 
-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 food101 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 the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.6196
-- Accuracy: 0.884
+- Loss: nan
+- Accuracy: 0.1562
 
 ## Model description
 
@@ -52,29 +52,26 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 200
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 64
 - 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
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.7166        | 0.99  | 62   | 2.5345          | 0.842    |
-| 1.7982        | 2.0   | 125  | 1.7848          | 0.876    |
-| 1.5772        | 2.98  | 186  | 1.6252          | 0.894    |
+| No log        | 1.0   | 40   | nan             | 0.1562   |
+| No log        | 2.0   | 80   | nan             | 0.1562   |
+| No log        | 3.0   | 120  | nan             | 0.1562   |
 
 
 ### Framework versions
 
-- Transformers 4.33.1
+- Transformers 4.33.2
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.5
 - Tokenizers 0.13.3