Allocated updated pycocotools metrics fix (#101)

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>

requirements.txt

@@ -40,7 +40,7 @@ ipython  # interactive notebook
 psutil  # system utilization
 thop>=0.1.1  # FLOPs computation
 # albumentations>=1.0.3
-# pycocotools>=2.0  # COCO mAP
+# pycocotools>=2.0.6  # COCO mAP
 # roboflow
 
 # HUB -----------------------------------------

ultralytics/yolo/engine/validator.py

@@ -127,7 +127,7 @@ class BaseValidator:
                     self.logger.info(f"Saving {f.name}...")
                     json.dump(self.jdict, f)  # flatten and save
 
-            self.eval_json()
+            stats = self.eval_json(stats)
             return stats
 
     def get_dataloader(self, dataset_path, batch_size):
@@ -171,5 +171,5 @@ class BaseValidator:
     def pred_to_json(self, preds, batch):
         pass
 
-    def eval_json(self):
+    def eval_json(self, stats):
         pass

ultralytics/yolo/v8/detect/val.py

@@ -210,13 +210,13 @@ class DetectionValidator(BaseValidator):
                     'bbox': [round(x, 3) for x in b],
                     'score': round(p[4], 5)})
 
-    def eval_json(self):
+    def eval_json(self, stats):
         if self.args.save_json and self.is_coco and len(self.jdict):
             anno_json = self.data['path'] / "annotations/instances_val2017.json"  # annotations
             pred_json = self.save_dir / "predictions.json"  # predictions
             self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
             try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
-                check_requirements('pycocotools')
+                check_requirements('pycocotools>=2.0.6')
                 from pycocotools.coco import COCO  # noqa
                 from pycocotools.cocoeval import COCOeval  # noqa
 
@@ -230,9 +230,10 @@ class DetectionValidator(BaseValidator):
                 eval.evaluate()
                 eval.accumulate()
                 eval.summarize()
-                self.metrics.metric.map, self.metrics.metric.map50 = eval.stats[:2]  # update mAP50-95 and mAP50
+                stats[self.metric_keys[-1]], stats[self.metric_keys[-2]] = eval.stats[:2]  # update mAP50-95 and mAP50
             except Exception as e:
                 self.logger.warning(f'pycocotools unable to run: {e}')
+        return stats
 
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)