ALM-AHME
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convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled

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ALM-AHME commited on Jul 21, 2023

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update model card README.md

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@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0946
-- Accuracy: 0.9852
 ## Model description
@@ -46,24 +46,27 @@ 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: 0.9
-- num_epochs: 12
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.9014        | 1.0   | 114  | 1.8872          | 0.3982   |
-| 1.6303        | 2.0   | 229  | 1.6163          | 0.5928   |
-| 1.291         | 3.0   | 343  | 1.2220          | 0.6773   |
-| 1.0813        | 4.0   | 458  | 0.9574          | 0.7750   |
-| 0.7168        | 5.0   | 572  | 0.7792          | 0.7603   |
-| 0.6184        | 6.0   | 687  | 0.5539          | 0.8678   |
-| 0.677         | 7.0   | 801  | 0.4482          | 0.8727   |
-| 0.4876        | 8.0   | 916  | 0.3289          | 0.9269   |
-| 0.4           | 9.0   | 1030 | 0.2379          | 0.9499   |
-| 0.4122        | 10.0  | 1145 | 0.2452          | 0.9351   |
-| 0.4494        | 11.0  | 1259 | 0.1790          | 0.9581   |
-| 0.2026        | 11.95 | 1368 | 0.0946          | 0.9852   |
 ### Framework versions

 This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.0721
+- Accuracy: 0.9869
 ## Model description
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.9
+- num_epochs: 15
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.8937        | 1.0   | 114  | 1.9040          | 0.3144   |
+| 1.7208        | 2.0   | 229  | 1.6891          | 0.5632   |
+| 1.3822        | 3.0   | 343  | 1.3554          | 0.6897   |
+| 1.1497        | 4.0   | 458  | 1.2437          | 0.5755   |
+| 0.8979        | 5.0   | 572  | 0.8548          | 0.7701   |
+| 0.6382        | 6.0   | 687  | 0.6359          | 0.8424   |
+| 0.583         | 7.0   | 801  | 0.4687          | 0.8966   |
+| 0.6295        | 8.0   | 916  | 0.5029          | 0.8456   |
+| 0.5367        | 9.0   | 1030 | 0.4742          | 0.8670   |
+| 0.5091        | 10.0  | 1145 | 0.3038          | 0.9212   |
+| 0.3521        | 11.0  | 1259 | 0.1855          | 0.9606   |
+| 0.318         | 12.0  | 1374 | 0.1893          | 0.9573   |
+| 0.2725        | 13.0  | 1488 | 0.2292          | 0.9409   |
+| 0.2937        | 14.0  | 1603 | 0.0866          | 0.9836   |
+| 0.1185        | 14.93 | 1710 | 0.0721          | 0.9869   |
 ### Framework versions