+ add tflite export

+ add Quantisierung

Export.py

@@ -7,4 +7,10 @@ dir = os.path.dirname(os.path.realpath(__file__))
 model = YOLO(dir + "/best.pt")
 
 # Exportiere es ins TFLite-Format
-model.export(format="tflite")
+model.export(
+    format="tflite",
+    dynamic=False,
+    inference_type="uint8",
+    quantize=True,
+    calibration_data="path/to/rep_dataset/"
+)

requirements.txt

@@ -0,0 +1,89 @@
+absl-py==2.3.0
+ai-edge-litert==1.3.0
+astunparse==1.6.3
+backports.strenum==1.2.8
+certifi==2025.4.26
+charset-normalizer==3.4.2
+coloredlogs==15.0.1
+contourpy==1.3.2
+cycler==0.12.1
+filelock==3.18.0
+flatbuffers==25.2.10
+fonttools==4.58.2
+fsspec==2025.5.1
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.73.0
+h5py==3.14.0
+humanfriendly==10.0
+idna==3.10
+Jinja2==3.1.6
+keras==3.10.0
+kiwisolver==1.4.8
+libclang==18.1.1
+Markdown==3.8
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.3
+mdurl==0.1.2
+ml_dtypes==0.5.1
+mpmath==1.3.0
+namex==0.1.0
+networkx==3.5
+numpy==2.1.3
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cuda-cupti-cu12==12.6.80
+nvidia-cuda-nvrtc-cu12==12.6.77
+nvidia-cuda-runtime-cu12==12.6.77
+nvidia-cudnn-cu12==9.5.1.17
+nvidia-cufft-cu12==11.3.0.4
+nvidia-cufile-cu12==1.11.1.6
+nvidia-curand-cu12==10.3.7.77
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cusparse-cu12==12.5.4.2
+nvidia-cusparselt-cu12==0.6.3
+nvidia-nccl-cu12==2.26.2
+nvidia-nvjitlink-cu12==12.6.85
+nvidia-nvtx-cu12==12.6.77
+onnx==1.17.0
+onnx2tf==1.27.10
+onnx_graphsurgeon==0.5.8
+onnxruntime==1.22.0
+onnxslim==0.1.57
+opencv-python==4.11.0.86
+opt_einsum==3.4.0
+optree==0.16.0
+packaging==25.0
+pandas==2.3.0
+pillow==11.2.1
+protobuf==5.29.5
+psutil==7.0.0
+py-cpuinfo==9.0.0
+Pygments==2.19.1
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.2
+requests==2.32.4
+rich==14.0.0
+scipy==1.15.3
+six==1.17.0
+sng4onnx==1.0.4
+sympy==1.14.0
+tensorboard==2.19.0
+tensorboard-data-server==0.7.2
+tensorflow==2.19.0
+tensorflow-io-gcs-filesystem==0.37.1
+termcolor==3.1.0
+tf_keras==2.19.0
+torch==2.7.1
+torchvision==0.22.1
+tqdm==4.67.1
+triton==3.3.1
+typing_extensions==4.14.0
+tzdata==2025.2
+ultralytics==8.3.154
+ultralytics-thop==2.0.14
+urllib3==2.4.0
+Werkzeug==3.1.3
+wrapt==1.17.2

test_tflite.py

@@ -0,0 +1,4 @@
+import tensorflow as tf
+
+interpreter = tf.lite.Interpreter(model_path="yolo_training/NAO_detector/weights/best_saved_model/best_float16.tflite")
+interpreter.allocate_tensors()

train_yolo.py

@@ -9,6 +9,8 @@ import numpy as np
 import torch
 import torchvision
 
+dir = os.path.dirname(os.path.realpath(__file__))
+
 def verify_images(dataset_path):
     """Überprüfe alle Bilder auf Lesbarkeit und korrekte Dimensionen."""
     print("\nÜberprüfe Bilder...")
@@ -79,7 +81,7 @@ def train_yolo():
     results = model.train(
         data=dataset_yaml,
         epochs=50,
-        imgsz=640,  # Bildgröße
+        imgsz=320,  # Bildgröße
         batch=16,    # Batch-Größe
         device=0,  # Verwende CPU (oder 'cuda' für GPU)
         patience=5,   # Early Stopping
@@ -92,10 +94,19 @@ def train_yolo():
     print("\nExportiere Modell in verschiedene Formate...")
     
     # ONNX Format (gut für eingebettete Systeme)
-    model.export(format='onnx', imgsz=640)
+    model.export(format='onnx', imgsz=320)
     
     # OpenVINO Format (optimiert für Intel Hardware)
-    model.export(format='openvino', imgsz=640)
+    model.export(format='openvino', imgsz=320)
+
+    model.export(
+        format="tflite",
+        dynamic=False,
+        inference_type="uint8",
+        quantize=True,
+        imgsz=320,
+        calibration_data=os.path.join(dir, "calib_images")
+    )
     
     print("\nTraining abgeschlossen. Die Modelle wurden im 'yolo_training/NAO_detector' Ordner gespeichert.")