get the script to export

yolo_export.py

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+from ultralytics import YOLO
+
+if __name__ == '__main__':
+    model = YOLO('yolov8n.pt')
+    model.export(format = 'saved_model', keras = True)
+    model.export(format = 'tflite')

yolo_run.py

@@ -0,0 +1,76 @@
+from ultralytics import YOLO
+"""
+Created on Wed Oct  4 16:44:12 2023
+
+@author: lin
+"""
+import glob
+import sys
+sys.path.append('../../..')
+import os
+import cv2
+import json
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+# from utils.bbox_op import non_max_supression
+
+def plot_prediction(image_np, bboxes, classes, scores, label_map):
+    color=(255,0,0)
+    thickness=5
+    font_scale=3
+
+    for i, box in enumerate(bboxes):
+        box = bboxes[i, :]
+        
+        x0, y0, x1, y1 = box
+
+        xmin = int(x0)
+        ymin = int(y0)
+        xmax = int(x1)
+        ymax = int(y1)
+
+        print(xmin, ymin, xmax, ymax)
+        
+        image_np = cv2.rectangle(image_np, (xmin, ymin), (xmax, ymax), color=color, thickness=thickness)
+        text_x = xmin - 10 if xmin > 20 else xmin + 10
+        text_y = ymin - 10 if ymin > 20 else ymin + 10
+        display_str = label_map[str(int(classes))]
+
+        cv2.putText(
+            image_np,
+            display_str,
+            (text_x, text_y),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale,
+            color,
+            thickness,
+        )
+    plt.imshow(image_np)
+    plt.show()
+
+if __name__ == "__main__":
+    min_th = 0.1
+    labels_json = "coco_labels.json"
+    with open(labels_json) as f:
+        label_map = json.load(f)
+    img_path = "test_images"
+    saved_tflite = "tflite_model.tflite"
+    # load model
+    
+    model = YOLO("yolov8n.pt")
+    images = glob.glob(os.path.join(img_path, "*"))
+    for img in images:
+        
+        image_np = cv2.imread(img) # gpreprocess(img)
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+        # print(image_np.shape)
+    
+        # image_np = np.array(Image.open(image_path))
+        results = model(img)
+        boxes = results[0].boxes
+
+        print(boxes.xyxy)
+        print(boxes.cls)
+        print(boxes.conf)
+        plot_prediction(image_np, boxes.xyxy.numpy(), boxes.cls.numpy(), boxes.conf.numpy(), label_map)

yolov8n.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd48a79dd7fec8ca25fde4eca742ff7bca23b27e2e903eb23bc1d9f83a459bd2
+size 12769720

yolov8n.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31e20dde3def09e2cf938c7be6fe23d9150bbbe503982af13345706515f2ef95
+size 6534387

yolov8n_saved_model/fingerprint.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b3492909f2891a54e9151b18733a1d42d514bea74240f213228f33eac6a017a
+size 56

yolov8n_saved_model/metadata.yaml

@@ -0,0 +1,92 @@
+description: Ultralytics YOLOv8n model trained on coco.yaml
+author: Ultralytics
+license: AGPL-3.0 https://ultralytics.com/license
+date: '2023-10-06T15:50:35.220556'
+version: 8.0.192
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush

yolov8n_saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f23f3eda89025c6d1da3916c7fed1a50179bb9182aedca2d6a74ba0f39fb2a23
+size 12913757

yolov8n_saved_model/yolov8n_float16.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72e4cbf64265e974923d6b723a2be1d1deeacbdc74aa770e5e82a9f0b753c645
+size 6434532

yolov8n_saved_model/yolov8n_float32.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:435e0b6375a32b862ed30cb630256d5dbefb9597c53c87125a66e44ab26cfd68
+size 12771680