Model save

README.md

@@ -24,10 +24,10 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9047619047619048
+      value: 0.09523809523809523
     - name: F1
       type: f1
-      value: 0.9032269317983602
+      value: 0.016563146997929608
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -37,9 +37,9 @@ 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: 0.8131
-- Accuracy: 0.9048
-- F1: 0.9032
+- Loss: nan
+- Accuracy: 0.0952
+- F1: 0.0166
 
 ## Model description
 
@@ -70,16 +70,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 2.0824        | 1.0   | 42   | 1.9709          | 0.4476   | 0.3861 |
-| 1.77          | 2.0   | 84   | 1.7102          | 0.6952   | 0.7022 |
-| 1.4691        | 3.0   | 126  | 1.4748          | 0.8476   | 0.8506 |
-| 1.198         | 4.0   | 168  | 1.2657          | 0.8952   | 0.8954 |
-| 0.9858        | 5.0   | 210  | 1.1113          | 0.9048   | 0.9035 |
-| 0.819         | 6.0   | 252  | 0.9877          | 0.9048   | 0.9032 |
-| 0.6969        | 7.0   | 294  | 0.9090          | 0.9048   | 0.9034 |
-| 0.6209        | 8.0   | 336  | 0.8524          | 0.9048   | 0.9032 |
-| 0.5651        | 9.0   | 378  | 0.8226          | 0.9048   | 0.9032 |
-| 0.5326        | 10.0  | 420  | 0.8131          | 0.9048   | 0.9032 |
+| 0.0           | 1.0   | 42   | nan             | 0.0952   | 0.0166 |
+| 0.0           | 2.0   | 84   | nan             | 0.0952   | 0.0166 |
+| 0.0           | 3.0   | 126  | nan             | 0.0952   | 0.0166 |
+| 0.0           | 4.0   | 168  | nan             | 0.0952   | 0.0166 |
+| 0.0           | 5.0   | 210  | nan             | 0.0952   | 0.0166 |
+| 0.0           | 6.0   | 252  | nan             | 0.0952   | 0.0166 |
+| 0.0           | 7.0   | 294  | nan             | 0.0952   | 0.0166 |
+| 0.0           | 8.0   | 336  | nan             | 0.0952   | 0.0166 |
+| 0.0           | 9.0   | 378  | nan             | 0.0952   | 0.0166 |
+| 0.0           | 10.0  | 420  | nan             | 0.0952   | 0.0166 |
 
 
 ### Framework versions

all_results.json

@@ -8,7 +8,7 @@
     "eval_steps_per_second": 2.149,
     "total_flos": 5.130291560557363e+17,
     "train_loss": 0.0,
-    "train_runtime": 523.0916,
-    "train_samples_per_second": 12.656,
+    "train_runtime": 522.9497,
+    "train_samples_per_second": 12.659,
     "train_steps_per_second": 0.803
 }

train_results.json

@@ -2,7 +2,7 @@
     "epoch": 10.0,
     "total_flos": 5.130291560557363e+17,
     "train_loss": 0.0,
-    "train_runtime": 523.0916,
-    "train_samples_per_second": 12.656,
+    "train_runtime": 522.9497,
+    "train_samples_per_second": 12.659,
     "train_steps_per_second": 0.803
 }