update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8373493975903614
+      value: 0.963855421686747
     - name: Precision
       type: precision
-      value: 0.8745971666076694
+      value: 0.9609609235289817
     - name: Recall
       type: recall
-      value: 0.7993336310123969
+      value: 0.9613676432460462
     - name: F1
       type: f1
-      value: 0.8036849674785987
+      value: 0.9604284776111401
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ 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 imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.1993
-- Accuracy: 0.8373
-- Precision: 0.8746
-- Recall: 0.7993
-- F1: 0.8037
+- Loss: 0.3076
+- Accuracy: 0.9639
+- Precision: 0.9610
+- Recall: 0.9614
+- F1: 0.9604
 
 ## Model description
 
@@ -75,17 +75,32 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 5
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| No log        | 1.0   | 3    | 1.6294          | 0.6747   | 0.6434    | 0.6238 | 0.5944 |
-| No log        | 2.0   | 6    | 1.4495          | 0.7530   | 0.7776    | 0.7018 | 0.6875 |
-| No log        | 3.0   | 9    | 1.3163          | 0.8373   | 0.8563    | 0.7993 | 0.8022 |
-| 1.5378        | 4.0   | 12   | 1.2327          | 0.8373   | 0.8736    | 0.7993 | 0.8035 |
-| 1.5378        | 5.0   | 15   | 1.1993          | 0.8373   | 0.8746    | 0.7993 | 0.8037 |
+| No log        | 1.0   | 3    | 1.2753          | 0.8373   | 0.8563    | 0.7993 | 0.8022 |
+| No log        | 2.0   | 6    | 1.1252          | 0.8675   | 0.8895    | 0.8300 | 0.8333 |
+| No log        | 3.0   | 9    | 0.9427          | 0.8976   | 0.9185    | 0.8696 | 0.8760 |
+| 1.1721        | 4.0   | 12   | 0.7995          | 0.9398   | 0.9474    | 0.9195 | 0.9246 |
+| 1.1721        | 5.0   | 15   | 0.6820          | 0.9699   | 0.9704    | 0.9613 | 0.9642 |
+| 1.1721        | 6.0   | 18   | 0.5927          | 0.9639   | 0.9603    | 0.9583 | 0.9587 |
+| 0.7084        | 7.0   | 21   | 0.5239          | 0.9759   | 0.9725    | 0.9729 | 0.9725 |
+| 0.7084        | 8.0   | 24   | 0.4743          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
+| 0.7084        | 9.0   | 27   | 0.4436          | 0.9578   | 0.9558    | 0.9556 | 0.9544 |
+| 0.4668        | 10.0  | 30   | 0.4070          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
+| 0.4668        | 11.0  | 33   | 0.3817          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
+| 0.4668        | 12.0  | 36   | 0.3625          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
+| 0.4668        | 13.0  | 39   | 0.3536          | 0.9578   | 0.9558    | 0.9556 | 0.9544 |
+| 0.3611        | 14.0  | 42   | 0.3384          | 0.9578   | 0.9558    | 0.9556 | 0.9544 |
+| 0.3611        | 15.0  | 45   | 0.3249          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
+| 0.3611        | 16.0  | 48   | 0.3164          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
+| 0.3063        | 17.0  | 51   | 0.3142          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
+| 0.3063        | 18.0  | 54   | 0.3122          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
+| 0.3063        | 19.0  | 57   | 0.3093          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
+| 0.294         | 20.0  | 60   | 0.3076          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
 
 
 ### Framework versions