Model save

README.md

@@ -2,7 +2,6 @@
 license: apache-2.0
 base_model: google/vit-base-patch16-224
 tags:
-- image-classification
 - generated_from_trainer
 datasets:
 - imagefolder
@@ -18,7 +17,7 @@ model-index:
       name: Image Classification
       type: image-classification
     dataset:
-      name: Mahadih534/brain-tumor-dataset
+      name: imagefolder
       type: imagefolder
       config: default
       split: train
@@ -26,16 +25,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7692307692307693
+      value: 0.9230769230769231
     - name: Precision
       type: precision
-      value: 0.7692307692307693
+      value: 0.9230769230769231
     - name: Recall
       type: recall
-      value: 0.7692307692307693
+      value: 0.9230769230769231
     - name: F1
       type: f1
-      value: 0.7692307692307693
+      value: 0.9230769230769231
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vit-base-oxford-brain-tumor_x-ray
 
-This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Mahadih534/brain-tumor-dataset dataset.
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5912
-- Accuracy: 0.7692
-- Precision: 0.7692
-- Recall: 0.7692
-- F1: 0.7692
+- Loss: 0.2882
+- Accuracy: 0.9231
+- Precision: 0.9231
+- Recall: 0.9231
+- F1: 0.9231
 
 ## Model description
 
@@ -74,22 +73,21 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 4
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 0.6752        | 1.0   | 11   | 0.4894          | 0.76     | 0.7148    | 0.76   | 0.7114 |
-| 0.5673        | 2.0   | 22   | 0.4630          | 0.72     | 0.57      | 0.72   | 0.6363 |
-| 0.6173        | 3.0   | 33   | 0.4269          | 0.92     | 0.92      | 0.92   | 0.92   |
-| 0.5562        | 4.0   | 44   | 0.5047          | 0.84     | 0.8653    | 0.84   | 0.8470 |
-| 0.5285        | 5.0   | 55   | 0.4036          | 0.92     | 0.92      | 0.92   | 0.92   |
+| 0.6519        | 1.0   | 11   | 0.3817          | 0.8      | 0.8476    | 0.8    | 0.7751 |
+| 0.2616        | 2.0   | 22   | 0.0675          | 0.96     | 0.9624    | 0.96   | 0.9594 |
+| 0.1219        | 3.0   | 33   | 0.1770          | 0.92     | 0.9289    | 0.92   | 0.9174 |
+| 0.0527        | 4.0   | 44   | 0.0234          | 1.0      | 1.0       | 1.0    | 1.0    |
 
 
 ### Framework versions
 
 - Transformers 4.41.2
 - Pytorch 2.3.0+cu121
-- Datasets 2.19.2
+- Datasets 2.20.0
 - Tokenizers 0.19.1

runs/Jun18_14-46-45_f111979319ff/events.out.tfevents.1718723911.f111979319ff.190.7

@@ -0,0 +1,3 @@
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+size 551