Model save

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6666666666666666
+      value: 0.8819264629725531
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,12 +33,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 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: 0.9763
-- Accuracy: 0.6667
-- Auc Overall: 0.7083
-- Auc Class 0: 0.625
-- Auc Class 1: 0.5
-- Auc Class 2: 1.0
+- Loss: 0.3063
+- Accuracy: 0.8819
+- Auc Overall: 0.9577
+- Auc Class 0: 0.9496
+- Auc Class 1: 0.9589
+- Auc Class 2: 0.9646
 
 ## Model description
 
@@ -66,7 +66,7 @@ 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: 3
+- num_epochs: 7
 
 ### Training results
 

all_results.json

@@ -1,5 +1,5 @@
 {
-    "epoch": 3.0,
+    "epoch": 6.885245901639344,
     "eval_accuracy": 0.6666666666666666,
     "eval_auc_class_0": 0.625,
     "eval_auc_class_1": 0.5,
@@ -10,13 +10,13 @@
     "eval_samples_per_second": 8.67,
     "eval_steps_per_second": 1.445,
     "total_flos": 3525591661885440.0,
-    "train_accuracy": 0.7333333333333333,
-    "train_auc_class_0": 0.8400000000000001,
-    "train_auc_class_1": 0.9199999999999999,
-    "train_auc_class_2": 1.0,
-    "train_auc_overall": 0.9199999999999999,
-    "train_loss": 0.24405852953592935,
-    "train_runtime": 72.8636,
-    "train_samples_per_second": 0.618,
-    "train_steps_per_second": 0.041
+    "train_accuracy": 0.8871655680496316,
+    "train_auc_class_0": 0.9601424899214485,
+    "train_auc_class_1": 0.968345723853034,
+    "train_auc_class_2": 0.9621813081120092,
+    "train_auc_overall": 0.9635565072954971,
+    "train_loss": 0.2833240032196045,
+    "train_runtime": 718.0615,
+    "train_samples_per_second": 10.775,
+    "train_steps_per_second": 0.085
 }

runs/Sep04_12-03-40_data-science-wbi/events.out.tfevents.1725464007.data-science-wbi

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c988fe0513fee6034c801929b81fb0986b7fa57847f1f7360f526ced36d55be
+size 152

train_results.json

@@ -1,8 +1,12 @@
 {
-    "epoch": 3.0,
-    "total_flos": 3525591661885440.0,
-    "train_loss": 0.24405852953592935,
-    "train_runtime": 72.8636,
-    "train_samples_per_second": 0.618,
-    "train_steps_per_second": 0.041
+    "epoch": 6.885245901639344,
+    "train_accuracy": 0.8871655680496316,
+    "train_auc_class_0": 0.9601424899214485,
+    "train_auc_class_1": 0.968345723853034,
+    "train_auc_class_2": 0.9621813081120092,
+    "train_auc_overall": 0.9635565072954971,
+    "train_loss": 0.2833240032196045,
+    "train_runtime": 718.0615,
+    "train_samples_per_second": 10.775,
+    "train_steps_per_second": 0.085
 }