feat add tflite model

onnx2tflite.py

@@ -7,7 +7,7 @@ model = YOLO("runs/detect/train/weights/best.pt")
 model.export(format="tflite")  # creates 'yolo11n_float32.tflite'
 
 # Load the exported TFLite model
-tflite_model = YOLO("runs/detect/train/weights/best.tflite")
+tflite_model = YOLO("runs/detect/train/weights/best_saved_model/best_float32.tflite")
 
 # Run inference
 results = tflite_model("datasets/valid/images/2007_000042_jpg.rf.dadefef10ec4b9422a1689f7ce40588e.jpg")

runs/detect/train/weights/best_saved_model/fingerprint.pb

@@ -0,0 +1 @@
+此<>艤𡡞]均<>齛<EFBFBD><18>嬰膋<E5ACB0> <20>馰<EFBFBD>樭(高<><E9AB98>闀<EFBFBD>
2
:3350961098177553315

runs/detect/train/weights/best_saved_model/metadata.yaml

@@ -0,0 +1,40 @@
+description: Ultralytics YOLO12s model trained on data.yaml
+author: Ultralytics
+date: '2025-06-04T03:14:12.116915'
+version: 8.3.148
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: aeroplane
+  1: bicycle
+  2: bird
+  3: boat
+  4: bottle
+  5: bus
+  6: car
+  7: cat
+  8: chair
+  9: cow
+  10: diningtable
+  11: dog
+  12: horse
+  13: motorbike
+  14: person
+  15: pottedplant
+  16: sheep
+  17: sofa
+  18: train
+  19: tvmonitor
+args:
+  batch: 1
+  fraction: 1.0
+  half: false
+  int8: false
+  nms: false
+channels: 3