End of training

README.md

@@ -1,6 +1,6 @@
 ---
 license: mit
-base_model: shi-labs/nat-small-in1k-224
+base_model: shi-labs/nat-mini-in1k-224
 tags:
 - generated_from_trainer
 datasets:
@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6625388813897529
+      value: 0.6347497903270898
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # msi
 
-This model is a fine-tuned version of [shi-labs/nat-small-in1k-224](https://huggingface.co/shi-labs/nat-small-in1k-224) on the imagefolder dataset.
+This model is a fine-tuned version of [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.6797
-- Accuracy: 0.6625
+- Loss: 1.4313
+- Accuracy: 0.6347
 
 ## Model description
 
@@ -52,7 +52,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-05
+- learning_rate: 5e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
@@ -61,27 +61,20 @@ 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: 10
+- num_epochs: 3
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 0.4035        | 1.0   | 1858  | 0.7665          | 0.6671   |
-| 0.2976        | 2.0   | 3717  | 1.0836          | 0.6372   |
-| 0.2552        | 3.0   | 5575  | 1.1942          | 0.6377   |
-| 0.219         | 4.0   | 7434  | 1.3987          | 0.6419   |
-| 0.1863        | 5.0   | 9292  | 1.5862          | 0.6248   |
-| 0.1946        | 6.0   | 11151 | 1.4975          | 0.6848   |
-| 0.1679        | 7.0   | 13009 | 1.6209          | 0.6518   |
-| 0.1531        | 8.0   | 14868 | 1.6400          | 0.6599   |
-| 0.15          | 9.0   | 16726 | 1.6733          | 0.6702   |
-| 0.1377        | 10.0  | 18580 | 1.6797          | 0.6625   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.2585        | 1.0   | 2015 | 1.1175          | 0.6088   |
+| 0.1938        | 2.0   | 4031 | 1.3762          | 0.6270   |
+| 0.1452        | 3.0   | 6045 | 1.4313          | 0.6347   |
 
 
 ### Framework versions
 
-- Transformers 4.35.2
-- Pytorch 2.0.1+cu118
+- Transformers 4.36.0
+- Pytorch 2.0.1
 - Datasets 2.15.0
 - Tokenizers 0.15.0