SJChaudhuri
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beit-base-patch16-224-pt22k-finetuned-eurosat

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SJChaudhuri commited on Apr 1

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update model card README.md

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@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.3418
-- Accuracy: 0.4048
 ## Model description
@@ -45,15 +45,32 @@ 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: 3
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 3    | 1.4733          | 0.2857   |
-| No log        | 2.0   | 6    | 1.3845          | 0.2857   |
-| No log        | 3.0   | 9    | 1.3418          | 0.4048   |
 ### Framework versions

 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.8454
+- Accuracy: 0.5714
 ## 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   | 3    | 1.3657          | 0.3095   |
+| No log        | 2.0   | 6    | 1.1966          | 0.4286   |
+| No log        | 3.0   | 9    | 1.1076          | 0.4524   |
+| 1.2696        | 4.0   | 12   | 1.0717          | 0.5714   |
+| 1.2696        | 5.0   | 15   | 0.9948          | 0.5238   |
+| 1.2696        | 6.0   | 18   | 1.0701          | 0.5      |
+| 1.0945        | 7.0   | 21   | 0.9920          | 0.5      |
+| 1.0945        | 8.0   | 24   | 0.9338          | 0.6667   |
+| 1.0945        | 9.0   | 27   | 0.9605          | 0.5714   |
+| 0.9538        | 10.0  | 30   | 0.9285          | 0.6190   |
+| 0.9538        | 11.0  | 33   | 0.9113          | 0.5714   |
+| 0.9538        | 12.0  | 36   | 0.8414          | 0.6190   |
+| 0.9538        | 13.0  | 39   | 0.9422          | 0.5476   |
+| 0.8646        | 14.0  | 42   | 0.8165          | 0.6429   |
+| 0.8646        | 15.0  | 45   | 0.9582          | 0.5238   |
+| 0.8646        | 16.0  | 48   | 0.8548          | 0.6190   |
+| 0.8082        | 17.0  | 51   | 0.8568          | 0.6190   |
+| 0.8082        | 18.0  | 54   | 0.8792          | 0.5476   |
+| 0.8082        | 19.0  | 57   | 0.8819          | 0.5476   |
+| 0.7731        | 20.0  | 60   | 0.8454          | 0.5714   |
 ### Framework versions