Add correct/incorrect to summary card for classification/non-binary e…

…valuations
voxel51 · Jan 14, 2025 · c0fd4b7 · c0fd4b7
1 parent a42cd44
commit c0fd4b7
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diff --git a/app/packages/core/src/plugins/SchemaIO/components/NativeModelEvaluationView/Evaluation.tsx b/app/packages/core/src/plugins/SchemaIO/components/NativeModelEvaluationView/Evaluation.tsx
@@ -210,6 +210,8 @@ export default function Evaluation(props: EvaluationProps) {
   const isBinaryClassification =
     evaluationType === "classification" && evaluationMethod === "binary";
   const showTpFpFn = isObjectDetection || isBinaryClassification;
+  const isNoneBinaryClassification =
+    isClassification && evaluationMethod !== "binary";
   const infoRows = [
     {
       id: "evaluation_key",
@@ -465,6 +467,24 @@ export default function Evaluation(props: EvaluationProps) {
           : false,
       hide: !showTpFpFn,
     },
+    {
+      id: true,
+      property: "Correct",
+      value: evaluationMetrics.num_correct,
+      compareValue: compareEvaluationMetrics.num_correct,
+      lesserIsBetter: false,
+      filterable: true,
+      hide: !isNoneBinaryClassification,
+    },
+    {
+      id: false,
+      property: "Incorrect",
+      value: evaluationMetrics.num_incorrect,
+      compareValue: compareEvaluationMetrics.num_incorrect,
+      lesserIsBetter: false,
+      filterable: true,
+      hide: !isNoneBinaryClassification,
+    },
   ];
 
   const perClassPerformance = {};

diff --git a/plugins/panels/model_evaluation/__init__.py b/plugins/panels/model_evaluation/__init__.py
@@ -325,6 +325,11 @@ def get_mask_targets(self, dataset, gt_field):
 
         return None
 
+    def get_correct_incorrect(self, results):
+        correct = np.count_nonzero(results.ypred == results.ytrue)
+        incorrect = np.count_nonzero(results.ypred != results.ytrue)
+        return correct, incorrect
+
     def load_evaluation(self, ctx):
         view_state = ctx.panel.get_state("view") or {}
         eval_key = view_state.get("key")
@@ -362,6 +367,16 @@ def load_evaluation(self, ctx):
             )
             metrics["mAP"] = self.get_map(results)
             metrics["mAR"] = self.get_mar(results)
+
+            if (
+                info.config.type == "classification"
+                and info.config.method != "binary"
+            ):
+                (
+                    metrics["num_correct"],
+                    metrics["num_incorrect"],
+                ) = self.get_correct_incorrect(results)
+
             evaluation_data = {
                 "metrics": metrics,
                 "info": serialized_info,