Metrics and loss structure (#28)

Co-authored-by: Ayush Chaurasia <ayush.chuararsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/yolo/v8/classify/val.py

@@ -0,0 +1,18 @@
+import torch
+
+from ultralytics import yolo
+
+
+class ClassificationValidator(yolo.BaseValidator):
+
+    def init_metrics(self):
+        self.correct = torch.tensor([])
+
+    def update_metrics(self, preds, targets):
+        correct_in_batch = (targets[:, None] == preds).float()
+        self.correct = torch.cat((self.correct, correct_in_batch))
+
+    def get_stats(self):
+        acc = torch.stack((self.correct[:, 0], self.correct.max(1).values), dim=1)  # (top1, top5) accuracy
+        top1, top5 = acc.mean(0).tolist()
+        return {"top1": top1, "top5": top5, "fitness": top5}