jayanthspratap
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convnext-tiny-224-drfx-CT-classifier

Image Classification

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Eval Results (legacy)

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jayanthspratap commited on May 27, 2023

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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.5294117647058824
 ---
 <!-- 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 [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7017
-- Accuracy: 0.5294
 ## Model description
@@ -60,17 +60,27 @@ 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: 0.1
-- num_epochs: 5
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 4    | 0.7158          | 0.4118   |
-| No log        | 2.0   | 8    | 0.7130          | 0.5882   |
-| 0.6797        | 3.0   | 12   | 0.7115          | 0.5882   |
-| 0.6797        | 4.0   | 16   | 0.7026          | 0.5294   |
-| 0.6529        | 5.0   | 20   | 0.7017          | 0.5294   |
 ### Framework versions

     metrics:
     - name: Accuracy
       type: accuracy
+      value: 0.7647058823529411
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.6452
+- Accuracy: 0.7647
 ## 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: 15
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 4    | 0.7084          | 0.5882   |
+| No log        | 2.0   | 8    | 0.7021          | 0.5294   |
+| 0.6143        | 3.0   | 12   | 0.7024          | 0.4118   |
+| 0.6143        | 4.0   | 16   | 0.6856          | 0.5294   |
+| 0.6154        | 5.0   | 20   | 0.6955          | 0.5294   |
+| 0.6154        | 6.0   | 24   | 0.7119          | 0.5294   |
+| 0.6154        | 7.0   | 28   | 0.7086          | 0.5882   |
+| 0.5786        | 8.0   | 32   | 0.6967          | 0.5882   |
+| 0.5786        | 9.0   | 36   | 0.6773          | 0.6471   |
+| 0.5026        | 10.0  | 40   | 0.6537          | 0.7647   |
+| 0.5026        | 11.0  | 44   | 0.6439          | 0.7647   |
+| 0.5026        | 12.0  | 48   | 0.6390          | 0.7647   |
+| 0.5086        | 13.0  | 52   | 0.6434          | 0.7647   |
+| 0.5086        | 14.0  | 56   | 0.6425          | 0.7647   |
+| 0.448         | 15.0  | 60   | 0.6452          | 0.7647   |
 ### Framework versions