update model card README.md

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: microsoft/resnet-50
+base_model: google/vit-base-patch16-224
 tags:
 - image-classification
 - generated_from_trainer
@@ -23,7 +23,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9382716049382716
+      value: 0.9915611814345991
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,10 +31,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # camera-type
 
-This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1654
-- Accuracy: 0.9383
+- Loss: 0.0235
+- Accuracy: 0.9916
 
 ## Model description
 
@@ -65,16 +65,18 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.4597        | 0.5   | 200  | 0.2801          | 0.9242   |
-| 0.1375        | 0.99  | 400  | 0.1654          | 0.9383   |
-| 0.0795        | 1.49  | 600  | 0.1904          | 0.9383   |
-| 0.0686        | 1.98  | 800  | 0.1810          | 0.9453   |
-| 0.026         | 2.48  | 1000 | 0.2216          | 0.9400   |
-| 0.0495        | 2.97  | 1200 | 0.2096          | 0.9453   |
-| 0.0487        | 3.47  | 1400 | 0.2174          | 0.9436   |
-| 0.0268        | 3.96  | 1600 | 0.2304          | 0.9453   |
-| 0.0254        | 4.46  | 1800 | 0.2574          | 0.9400   |
-| 0.0186        | 4.95  | 2000 | 0.3212          | 0.9383   |
+| 0.0064        | 0.4   | 200  | 0.0235          | 0.9916   |
+| 0.0034        | 0.79  | 400  | 0.0392          | 0.9941   |
+| 0.0066        | 1.19  | 600  | 0.1011          | 0.9840   |
+| 0.0           | 1.58  | 800  | 0.1227          | 0.9840   |
+| 0.0           | 1.98  | 1000 | 0.1232          | 0.9840   |
+| 0.0           | 2.37  | 1200 | 0.1433          | 0.9840   |
+| 0.0           | 2.77  | 1400 | 0.1416          | 0.9840   |
+| 0.0           | 3.16  | 1600 | 0.1408          | 0.9840   |
+| 0.0           | 3.56  | 1800 | 0.1401          | 0.9840   |
+| 0.0           | 3.95  | 2000 | 0.1394          | 0.9840   |
+| 0.0           | 4.35  | 2200 | 0.1390          | 0.9840   |
+| 0.0           | 4.74  | 2400 | 0.1389          | 0.9840   |
 
 
 ### Framework versions