🍻 cheers

README.md

@@ -24,10 +24,10 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8589626933575978
+      value: 0.8171064604185623
     - name: F1
       type: f1
-      value: 0.8116646415552855
+      value: 0.7841031149301826
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -37,12 +37,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 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 stool-image dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3651
-- Auroc: 0.9090
-- Accuracy: 0.8590
-- Sensitivity: 0.7422
-- Specificty: 0.9399
-- F1: 0.8117
+- Loss: 0.4538
+- Auroc: 0.8897
+- Accuracy: 0.8171
+- Sensitivity: 0.8111
+- Specificty: 0.8213
+- F1: 0.7841
 
 ## Model description
 
@@ -67,15 +67,19 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 2
+- num_epochs: 100
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Auroc  | Accuracy | Sensitivity | Specificty | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:-----------:|:----------:|:------:|
-| 0.3775        | 0.98  | 100  | 0.4743          | 0.9005 | 0.7863   | 0.5899      | 0.9602     | 0.7216 |
-| 0.3238        | 1.96  | 200  | 0.4048          | 0.8973 | 0.8232   | 0.7360      | 0.9005     | 0.7964 |
+| 0.5303        | 0.98  | 100  | 0.4327          | 0.8826 | 0.7942   | 0.7191      | 0.8607     | 0.7665 |
+| 0.3909        | 1.96  | 200  | 0.5196          | 0.8675 | 0.8047   | 0.8539      | 0.7612     | 0.8042 |
+| 0.5328        | 2.94  | 300  | 0.4421          | 0.8864 | 0.8074   | 0.7528      | 0.8557     | 0.7859 |
+| 0.4834        | 3.92  | 400  | 0.4721          | 0.8596 | 0.7757   | 0.7135      | 0.8308     | 0.7493 |
+| 0.4209        | 4.9   | 500  | 0.4797          | 0.8625 | 0.7863   | 0.6798      | 0.8806     | 0.7492 |
+| 0.4567        | 5.88  | 600  | 0.5150          | 0.8688 | 0.7942   | 0.6011      | 0.9652     | 0.7329 |
 
 
 ### Framework versions

all_results.json

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eval_results.json

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