Add Dockerfile-conda FROM continuumio/miniconda3:latest (#4706)

ultralytics/engine/exporter.py

@@ -154,7 +154,8 @@ class Exporter:
         format = self.args.format.lower()  # to lowercase
         if format in ('tensorrt', 'trt'):  # 'engine' aliases
             format = 'engine'
-        if format in ('mlmodel', 'mlpackage', 'mlprogram', 'apple', 'ios'):  # 'coreml' aliases
+        if format in ('mlmodel', 'mlpackage', 'mlprogram', 'apple', 'ios', 'coreml'):  # 'coreml' aliases
+            os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'  # fix attempt for protobuf<3.20.x errors
             format = 'coreml'
         fmts = tuple(export_formats()['Argument'][1:])  # available export formats
         flags = [x == format for x in fmts]

ultralytics/engine/validator.py

@@ -24,7 +24,6 @@ from pathlib import Path
 
 import numpy as np
 import torch
-from scipy.optimize import linear_sum_assignment
 
 from ultralytics.cfg import get_cfg, get_save_dir
 from ultralytics.data.utils import check_cls_dataset, check_det_dataset
@@ -226,9 +225,11 @@ class BaseValidator:
         iou = iou.cpu().numpy()
         for i, threshold in enumerate(self.iouv.cpu().tolist()):
             if use_scipy:
+                # WARNING: known issue that reduces mAP in https://github.com/ultralytics/ultralytics/pull/4708
+                import scipy  # scope import to avoid importing for all commands
                 cost_matrix = iou * (iou >= threshold)
                 if cost_matrix.any():
-                    labels_idx, detections_idx = linear_sum_assignment(cost_matrix, maximize=True)
+                    labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix, maximize=True)
                     valid = cost_matrix[labels_idx, detections_idx] > 0
                     if valid.any():
                         correct[detections_idx[valid], i] = True