End of training

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.375
+      value: 0.51875
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.5119
-- Accuracy: 0.375
+- Loss: 1.5386
+- Accuracy: 0.5188
 
 ## Model description
 
@@ -52,23 +52,41 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 4
-- eval_batch_size: 4
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 16
+- total_train_batch_size: 32
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 2
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.691         | 1.0   | 40   | 1.7336          | 0.3      |
-| 1.41          | 2.0   | 80   | 1.4853          | 0.4562   |
+| 2.0473        | 1.0   | 20   | 2.0179          | 0.175    |
+| 1.6184        | 2.0   | 40   | 1.7787          | 0.2437   |
+| 1.2134        | 3.0   | 60   | 1.5985          | 0.3625   |
+| 1.0157        | 4.0   | 80   | 1.3311          | 0.4813   |
+| 0.8578        | 5.0   | 100  | 1.3041          | 0.4875   |
+| 0.6496        | 6.0   | 120  | 1.3222          | 0.5062   |
+| 0.5972        | 7.0   | 140  | 1.5594          | 0.4562   |
+| 0.5073        | 8.0   | 160  | 1.4126          | 0.4813   |
+| 0.3964        | 9.0   | 180  | 1.3702          | 0.525    |
+| 0.4054        | 10.0  | 200  | 1.3894          | 0.5188   |
+| 0.2845        | 11.0  | 220  | 1.4471          | 0.5188   |
+| 0.2262        | 12.0  | 240  | 1.5165          | 0.525    |
+| 0.2412        | 13.0  | 260  | 1.4684          | 0.5125   |
+| 0.2229        | 14.0  | 280  | 1.4005          | 0.525    |
+| 0.2078        | 15.0  | 300  | 1.5629          | 0.5062   |
+| 0.1619        | 16.0  | 320  | 1.6014          | 0.525    |
+| 0.1834        | 17.0  | 340  | 1.4821          | 0.5125   |
+| 0.1594        | 18.0  | 360  | 1.5195          | 0.5375   |
+| 0.1249        | 19.0  | 380  | 1.5585          | 0.5188   |
+| 0.1117        | 20.0  | 400  | 1.4735          | 0.5687   |
 
 
 ### Framework versions

runs/Sep04_12-51-37_9ffd3e1a146d/events.out.tfevents.1725454309.9ffd3e1a146d.889.0

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-size 19579
+oid sha256:e2ca4412f733ab7e145e01f0916050bb5789450f72882e0f9dd29077403e65c1
+size 20256

runs/Sep04_12-51-37_9ffd3e1a146d/events.out.tfevents.1725457105.9ffd3e1a146d.889.1

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