saketsarin
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vit-base-patch16-224-in21k_brain_tumor_diagnosis

Image Classification

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Model card Files Files and versions Metrics Training metrics Community

saketsarin commited on Nov 5, 2023

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README.md +14 -14

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@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 1.0
     - name: F1
       type: f1
-      value: 1.0
     - name: Recall
       type: recall
-      value: 1.0
     - name: Precision
       type: precision
-      value: 1.0
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0001
-- Accuracy: 1.0
-- F1: 1.0
-- Recall: 1.0
-- Precision: 1.0
 ## Model description
@@ -79,11 +79,11 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
-| 0.08          | 1.0   | 664  | 0.1355          | 0.9717   | 0.9735 | 1.0    | 0.9484    |
-| 0.0287        | 2.0   | 1328 | 0.0782          | 0.9838   | 0.9846 | 1.0    | 0.9697    |
-| 0.0083        | 3.0   | 1992 | 0.0085          | 0.9981   | 0.9982 | 0.9964 | 1.0       |
-| 0.0036        | 4.0   | 2656 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       |
-| 0.0001        | 5.0   | 3320 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       |
 ### Framework versions

     metrics:
     - name: Accuracy
       type: accuracy
+      value: 0.9857651245551602
     - name: F1
       type: f1
+      value: 0.9857500097665184
     - name: Recall
       type: recall
+      value: 0.9857651245551602
     - name: Precision
       type: precision
+      value: 0.9857741873841454
 ---
 <!-- 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 [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.0630
+- Accuracy: 0.9858
+- F1: 0.9858
+- Recall: 0.9858
+- Precision: 0.9858
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 1.379         | 1.0   | 352  | 0.2159          | 0.9310   | 0.9310 | 0.9310 | 0.9390    |
+| 0.239         | 2.0   | 704  | 0.0814          | 0.9765   | 0.9766 | 0.9765 | 0.9767    |
+| 0.0748        | 3.0   | 1056 | 0.0822          | 0.9808   | 0.9808 | 0.9808 | 0.9812    |
+| 0.0748        | 4.0   | 1408 | 0.0651          | 0.9858   | 0.9858 | 0.9858 | 0.9858    |
+| 0.0125        | 5.0   | 1760 | 0.0630          | 0.9858   | 0.9858 | 0.9858 | 0.9858    |
 ### Framework versions