End of training

README.md

@@ -16,12 +16,12 @@ model-index:
       name: fair_face
       type: fair_face
       config: '0.25'
-      split: train[:7000]
+      split: train[:10000]
       args: '0.25'
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.5707142857142857
+      value: 0.601
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier) on the fair_face dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0061
-- Accuracy: 0.5707
+- Loss: 0.9464
+- Accuracy: 0.601
 
 ## Model description
 
@@ -60,15 +60,17 @@ 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: 3
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.9702        | 0.99  | 87   | 0.9851          | 0.5657   |
-| 0.912         | 2.0   | 175  | 0.9593          | 0.58     |
-| 0.8171        | 2.98  | 261  | 1.0026          | 0.58     |
+| 0.9107        | 1.0   | 125  | 0.9360          | 0.6065   |
+| 0.7945        | 2.0   | 250  | 0.9545          | 0.588    |
+| 1.0256        | 3.0   | 375  | 1.0144          | 0.586    |
+| 0.7354        | 4.0   | 500  | 0.9726          | 0.594    |
+| 0.6979        | 5.0   | 625  | 0.9735          | 0.5995   |
 
 
 ### Framework versions

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:26178d82cb572b9379a023ed843823602a316daffd7fedac403312919dc7a4f8
 size 343288497