Model save

.DS_Store

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:827885eac2cd840b1f6eb85bb715d9f94710f281d55af24c8c6248c00d7e2419
-size 6148
+oid sha256:be7ee4c5499873a743ef098ec2e10c0c29cbf8f850ebeb763ce72c0762072adc
+size 10244

README.md

@@ -2,9 +2,38 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: swin-tiny-patch4-window7-224-finetuned-brainTumorData
-  results: []
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9977843426883308
+    - name: F1
+      type: f1
+      value: 0.9984067976633033
+    - name: Recall
+      type: recall
+      value: 0.9978768577494692
+    - name: Precision
+      type: precision
+      value: 0.9989373007438895
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -12,7 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # swin-tiny-patch4-window7-224-finetuned-brainTumorData
 
-This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.0101
+- Accuracy: 0.9978
+- F1: 0.9984
+- Recall: 0.9979
+- Precision: 0.9989
 
 ## Model description
 
@@ -42,6 +77,16 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.1
 - num_epochs: 4
 
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.1884        | 1.0   | 95   | 0.0706          | 0.9705   | 0.9787 | 0.9756 | 0.9818    |
+| 0.1134        | 2.0   | 190  | 0.0364          | 0.9889   | 0.9920 | 0.9883 | 0.9957    |
+| 0.1031        | 3.0   | 285  | 0.0116          | 0.9963   | 0.9973 | 0.9947 | 1.0       |
+| 0.0746        | 4.0   | 380  | 0.0101          | 0.9978   | 0.9984 | 0.9979 | 0.9989    |
+
+
 ### Framework versions
 
 - Transformers 4.23.1

all_results.json

@@ -1,13 +1,13 @@
 {
     "epoch": 4.0,
-    "eval_accuracy": 0.4357459379615953,
-    "eval_f1": 0.4203338391502276,
-    "eval_loss": 0.7448647022247314,
-    "eval_precision": 0.7759103641456583,
-    "eval_recall": 0.2882414151925078,
-    "eval_runtime": 70.275,
-    "eval_samples_per_second": 19.267,
-    "eval_steps_per_second": 0.612,
+    "eval_accuracy": 0.9977843426883308,
+    "eval_f1": 0.9984067976633033,
+    "eval_loss": 0.010051622986793518,
+    "eval_precision": 0.9989373007438895,
+    "eval_recall": 0.9978768577494692,
+    "eval_runtime": 66.6969,
+    "eval_samples_per_second": 20.301,
+    "eval_steps_per_second": 0.645,
     "total_flos": 3.037401854071603e+17,
     "train_loss": 0.2525144927203655,
     "train_runtime": 9271.044,

eval_results.json

@@ -1,10 +1,11 @@
 {
-    "eval_accuracy": 0.4357459379615953,
-    "eval_f1": 0.4203338391502276,
-    "eval_loss": 0.7448647022247314,
-    "eval_precision": 0.7759103641456583,
-    "eval_recall": 0.2882414151925078,
-    "eval_runtime": 70.275,
-    "eval_samples_per_second": 19.267,
-    "eval_steps_per_second": 0.612
+    "epoch": 4.0,
+    "eval_accuracy": 0.9977843426883308,
+    "eval_f1": 0.9984067976633033,
+    "eval_loss": 0.010051622986793518,
+    "eval_precision": 0.9989373007438895,
+    "eval_recall": 0.9978768577494692,
+    "eval_runtime": 66.6969,
+    "eval_samples_per_second": 20.301,
+    "eval_steps_per_second": 0.645
 }