dvs
/

swin-tiny-patch4-window7-224-mulder-v-scully-colab

@@ -31,7 +31,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5821
 - Accuracy: 0.75
 ## Model description
@@ -60,22 +60,32 @@ 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: 10
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 1    | 0.8688          | 0.25     |
-| No log        | 2.0   | 2    | 0.7693          | 0.25     |
-| No log        | 3.0   | 3    | 0.7056          | 0.5      |
-| No log        | 4.0   | 4    | 0.6579          | 0.5      |
-| No log        | 5.0   | 5    | 0.6105          | 0.75     |
-| No log        | 6.0   | 6    | 0.6010          | 0.75     |
-| No log        | 7.0   | 7    | 0.5963          | 0.75     |
-| No log        | 8.0   | 8    | 0.5913          | 0.75     |
-| No log        | 9.0   | 9    | 0.5851          | 0.75     |
-| 0.1959        | 10.0  | 10   | 0.5821          | 0.75     |
 ### Framework versions

 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.4114
 - Accuracy: 0.75
 ## Model description
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 1    | 0.6105          | 0.75     |
+| No log        | 2.0   | 2    | 0.6975          | 0.5      |
+| No log        | 3.0   | 3    | 0.8309          | 0.25     |
+| No log        | 4.0   | 4    | 0.7604          | 0.5      |
+| No log        | 5.0   | 5    | 0.6327          | 0.5      |
+| No log        | 6.0   | 6    | 0.5101          | 0.75     |
+| No log        | 7.0   | 7    | 0.4148          | 0.75     |
+| No log        | 8.0   | 8    | 0.3652          | 1.0      |
+| No log        | 9.0   | 9    | 0.3433          | 1.0      |
+| 0.0984        | 10.0  | 10   | 0.3231          | 1.0      |
+| 0.0984        | 11.0  | 11   | 0.3071          | 1.0      |
+| 0.0984        | 12.0  | 12   | 0.3047          | 1.0      |
+| 0.0984        | 13.0  | 13   | 0.3189          | 0.75     |
+| 0.0984        | 14.0  | 14   | 0.3437          | 0.75     |
+| 0.0984        | 15.0  | 15   | 0.3701          | 0.75     |
+| 0.0984        | 16.0  | 16   | 0.3959          | 0.75     |
+| 0.0984        | 17.0  | 17   | 0.4167          | 0.75     |
+| 0.0984        | 18.0  | 18   | 0.4190          | 0.75     |
+| 0.0984        | 19.0  | 19   | 0.4154          | 0.75     |
+| 0.0632        | 20.0  | 20   | 0.4114          | 0.75     |
 ### Framework versions