Update README.md

README.md

@@ -22,18 +22,30 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.5
+      value: 0.9960906958561376
+    - name: F1
+      type: f1
+      value: 0.9960906958561376
+    - name: Recall
+      type: recall
+      value: 0.9960906958561376
+    - name: Precision
+      type: precision
+      value: 0.9960906958561376
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# swin-tiny-patch4-window7-224-finetuned-brain-ich
+# Brain_Tumor_Classification_using_swin
 
-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.
+This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.5829
-- Accuracy: 0.5
+- Loss: 0.0123
+- Accuracy: 0.9961
+- F1: 0.9961
+- Recall: 0.9961
+- Precision: 0.9961
 
 ## Model description
 
@@ -61,27 +73,20 @@ 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
+- num_epochs: 3
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 1    | 1.6744          | 0.3571   |
-| No log        | 2.0   | 2    | 1.7141          | 0.2857   |
-| No log        | 3.0   | 3    | 1.7561          | 0.2857   |
-| No log        | 4.0   | 4    | 1.7594          | 0.3571   |
-| No log        | 5.0   | 5    | 1.7200          | 0.3571   |
-| No log        | 6.0   | 6    | 1.6617          | 0.3571   |
-| No log        | 7.0   | 7    | 1.6157          | 0.3571   |
-| No log        | 8.0   | 8    | 1.5829          | 0.5      |
-| No log        | 9.0   | 9    | 1.5618          | 0.5      |
-| 1.5896        | 10.0  | 10   | 1.5522          | 0.5      |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.1234        | 1.0   | 180  | 0.0450          | 0.9840   | 0.9840 | 0.9840 | 0.9840    |
+| 0.0837        | 2.0   | 360  | 0.0198          | 0.9926   | 0.9926 | 0.9926 | 0.9926    |
+| 0.0373        | 3.0   | 540  | 0.0123          | 0.9961   | 0.9961 | 0.9961 | 0.9961    |
 
 
 ### Framework versions
 
-- Transformers 4.35.2
-- Pytorch 2.1.0+cu121
-- Datasets 2.16.0
-- Tokenizers 0.15.0
+- Transformers 4.23.1
+- Pytorch 1.13.0
+- Datasets 2.6.1
+- Tokenizers 0.13.1