diff --git a/utils/onnx2coreml.py b/utils/onnx2coreml.py
new file mode 100644
index 00000000..3b9af6bd
--- /dev/null
+++ b/utils/onnx2coreml.py
@@ -0,0 +1,137 @@
+import os
+from onnx import onnx_pb
+from onnx_coreml import convert
+import glob
+
+
+# https://github.com/onnx/onnx-coreml
+# http://machinethink.net/blog/mobilenet-ssdlite-coreml/
+
+def main():
+    os.system('rm -rf saved_models && mkdir saved_models')
+    files = glob.glob('saved_models/*.onnx') + glob.glob('../yolov3/weights/*.onnx')
+
+    for f in files:
+        # 1. ONNX to CoreML
+        name = 'saved_models/' + f.split('/')[-1].replace('.onnx', '')
+
+        model_file = open(f, 'rb')
+        model_proto = onnx_pb.ModelProto()
+        model_proto.ParseFromString(model_file.read())
+        coreml_model = convert(model_proto, image_input_names=['0'])
+        # coreml_model.save(model_out)
+
+        # 2. Reduce model to FP16, change outputs to DOUBLE and save
+        import coremltools
+
+        spec = coreml_model.get_spec()
+        for i in range(2):
+            spec.description.output[i].type.multiArrayType.dataType = \
+                coremltools.proto.FeatureTypes_pb2.ArrayFeatureType.ArrayDataType.Value('DOUBLE')
+
+        spec = coremltools.utils.convert_neural_network_spec_weights_to_fp16(spec)
+        coreml_model = coremltools.models.MLModel(spec)
+
+        num_classes = 80
+        num_anchors = 507
+        spec.description.output[0].type.multiArrayType.shape.append(num_classes)
+        spec.description.output[0].type.multiArrayType.shape.append(num_anchors)
+
+        spec.description.output[1].type.multiArrayType.shape.append(4)
+        spec.description.output[1].type.multiArrayType.shape.append(num_anchors)
+        coreml_model.save(name + '.mlmodel')
+        print(spec.description)
+
+        # 3. Create NMS protobuf
+        import numpy as np
+
+        nms_spec = coremltools.proto.Model_pb2.Model()
+        nms_spec.specificationVersion = 3
+
+        for i in range(2):
+            decoder_output = coreml_model._spec.description.output[i].SerializeToString()
+
+            nms_spec.description.input.add()
+            nms_spec.description.input[i].ParseFromString(decoder_output)
+
+            nms_spec.description.output.add()
+            nms_spec.description.output[i].ParseFromString(decoder_output)
+
+        nms_spec.description.output[0].name = 'confidence'
+        nms_spec.description.output[1].name = 'coordinates'
+
+        output_sizes = [num_classes, 4]
+        for i in range(2):
+            ma_type = nms_spec.description.output[i].type.multiArrayType
+            ma_type.shapeRange.sizeRanges.add()
+            ma_type.shapeRange.sizeRanges[0].lowerBound = 0
+            ma_type.shapeRange.sizeRanges[0].upperBound = -1
+            ma_type.shapeRange.sizeRanges.add()
+            ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
+            ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
+            del ma_type.shape[:]
+
+        nms = nms_spec.nonMaximumSuppression
+        nms.confidenceInputFeatureName = '133'  # 1x507x80
+        nms.coordinatesInputFeatureName = '134'  # 1x507x4
+        nms.confidenceOutputFeatureName = 'confidence'
+        nms.coordinatesOutputFeatureName = 'coordinates'
+        nms.iouThresholdInputFeatureName = 'iouThreshold'
+        nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
+
+        nms.iouThreshold = 0.6
+        nms.confidenceThreshold = 0.4
+        nms.pickTop.perClass = True
+
+        labels = np.loadtxt('../yolov3/data/coco.names', dtype=str, delimiter='\n')
+        nms.stringClassLabels.vector.extend(labels)
+
+        nms_model = coremltools.models.MLModel(nms_spec)
+        nms_model.save(name + '_nms.mlmodel')
+
+        # 4. Pipeline models togethor
+        from coremltools.models import datatypes
+        # from coremltools.models import neural_network
+        from coremltools.models.pipeline import Pipeline
+
+        input_features = [('image', datatypes.Array(3, 416, 416)),
+                          ('iouThreshold', datatypes.Double()),
+                          ('confidenceThreshold', datatypes.Double())]
+
+        output_features = ['confidence', 'coordinates']
+
+        pipeline = Pipeline(input_features, output_features)
+
+        # Add 3rd dimension of size 1 (apparently not needed, produces error on compile)
+        # ssd_output = coreml_model._spec.description.output
+        # ssd_output[0].type.multiArrayType.shape[:] = [num_classes, num_anchors, 1]
+        # ssd_output[1].type.multiArrayType.shape[:] = [4, num_anchors, 1]
+
+        # And now we can add the three models, in order:
+        pipeline.add_model(coreml_model)
+        pipeline.add_model(nms_model)
+
+        # Correct datatypes
+        pipeline.spec.description.input[0].ParseFromString(coreml_model._spec.description.input[0].SerializeToString())
+        pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
+        pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
+
+        # Update metadata
+        pipeline.spec.description.metadata.versionString = 'yolov3-tiny.pt imported from PyTorch'
+        pipeline.spec.description.metadata.shortDescription = 'https://github.com/ultralytics/yolov3'
+        pipeline.spec.description.metadata.author = 'glenn.jocher@ultralytics.com'
+        pipeline.spec.description.metadata.license = 'https://github.com/ultralytics/yolov3'
+
+        user_defined_metadata = {'classes': ','.join(labels),
+                                 'iou_threshold': str(nms.iouThreshold),
+                                 'confidence_threshold': str(nms.confidenceThreshold)}
+        pipeline.spec.description.metadata.userDefined.update(user_defined_metadata)
+
+        # Save the model
+        pipeline.spec.specificationVersion = 3
+        final_model = coremltools.models.MLModel(pipeline.spec)
+        final_model.save((name + '_pipelined.mlmodel'))
+
+
+if __name__ == '__main__':
+    main()