Upload densenet121-chexnet-binary model (AUC: 0.6079)

README.md

@@ -57,12 +57,12 @@ Ce modèle a été entraîné pour la classification multi-label de pathologies
 
 | Metric | Value |
 |--------|-------|
-| AUC-ROC | 0.5729 |
-| F1-Score | N/A |
-| Precision | N/A |
-| Recall | N/A |
-| Accuracy | 0.9783 |
-| Specificity | 1.0000 |
+| AUC-ROC | 0.6079 |
+| F1-Score | 0.0609 |
+| Precision | 0.0328 |
+| Recall | 0.4288 |
+| Accuracy | 0.7130 |
+| Specificity | 0.7193 |
 
 
 ## 🚀 Utilisation

config.json

@@ -21,7 +21,7 @@
   "image_size": 224,
   "task": "multi-label-classification",
   "dataset": "NIH Chest X-ray",
-  "uploaded_at": "2025-12-04T22:24:34.217437",
+  "uploaded_at": "2025-12-06T02:16:44.980882",
   "training_config": {
     "num_epochs": 30,
     "learning_rate": 0.001,
@@ -29,16 +29,16 @@
     "early_stopping_patience": 30
   },
   "metrics": {
-    "f1": 0.0,
-    "precision": 0.0,
-    "recall": 0.0,
-    "accuracy": 0.9783169245194562,
-    "auc": 0.5729390106531594,
-    "sensitivity": 0.0,
-    "specificity": 0.9999999999996007,
-    "true_positives": 0,
-    "true_negatives": 25041,
-    "false_positives": 0,
-    "false_negatives": 555
+    "f1": 0.060861782380769724,
+    "precision": 0.03275529865125241,
+    "recall": 0.42882882882882883,
+    "accuracy": 0.7130411001719018,
+    "auc": 0.6079484420325442,
+    "sensitivity": 0.4288288288211022,
+    "specificity": 0.719340281937335,
+    "true_positives": 238,
+    "true_negatives": 18013,
+    "false_positives": 7028,
+    "false_negatives": 317
   }
 }

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b699b0ae4b77417667696cc545568df2ff4b5d35a99a21941a06bc69dda633a7
+oid sha256:f87b464fb9f0293f7041a69af6565d810334912d6768126c6aed0b0250b43a22
 size 28439487