tflite model trainiert und quantisiert

.gitignore

@@ -114,3 +114,4 @@ dmypy.json
 runs/
 Test/
 yolo_dataset/
+calib_images/

quantisierung.py

@@ -0,0 +1,42 @@
+import tensorflow as tf
+import numpy as np
+import os
+from PIL import Image
+
+dir = os.path.dirname(os.path.realpath(__file__))
+
+# --- Pfad zum SavedModel ---
+saved_model_dir = dir + "/yolo_training/NAO_detector9/weights/best_saved_model"
+
+# --- Optional: Pfad zu Beispielbildern ---
+image_dir = dir + "/calib_images"  # z. B. 50–100 JPGs aus deinem Datensatz (z. B. NAO-Roboter-Bilder)
+input_size = (320, 320)  # oder (640, 640) je nach deinem Modellinput
+
+# --- Repräsentative Datenfunktion ---
+def representative_data_gen():
+    for filename in os.listdir(image_dir):
+        if filename.endswith(".jpg") or filename.endswith(".png"):
+            img = Image.open(os.path.join(image_dir, filename)).convert("RGB")
+            img = img.resize(input_size)
+            img = np.array(img, dtype=np.float32) / 255.0  # normalisieren falls nötig
+            img = np.expand_dims(img, axis=0)
+            yield [img]
+
+# --- Konverter konfigurieren ---
+converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.representative_dataset = representative_data_gen
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+
+# Je nach Ziel-Hardware (z. B. NAO): INT8 in/out oder UINT8
+converter.inference_input_type = tf.uint8
+converter.inference_output_type = tf.uint8
+
+# --- Konvertieren ---
+quant_model = converter.convert()
+
+# --- Speichern ---
+with open("best_int8.tflite", "wb") as f:
+    f.write(quant_model)
+
+print("✅ INT8-Quantisierung abgeschlossen.")

train_yolo.py

@@ -99,14 +99,8 @@ def train_yolo():
     # OpenVINO Format (optimiert für Intel Hardware)
     model.export(format='openvino', imgsz=320)
 
-    model.export(
+    # tflite Format  
-        format="tflite",
+    model.export(format="tflite",imgsz=320,)
-        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.")
 

yolo_training/NAO_detector9/args.yaml

@@ -0,0 +1,105 @@
+task: detect
+mode: train
+model: yolov8n.pt
+data: yolo_dataset/dataset.yaml
+epochs: 50
+time: null
+patience: 5
+batch: 16
+imgsz: 320
+save: true
+save_period: -1
+cache: false
+device: '0'
+workers: 8
+project: yolo_training
+name: NAO_detector9
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+cutmix: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: yolo_training/NAO_detector9

yolo_training/NAO_detector9/results.csv

@@ -0,0 +1,31 @@
+epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
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+3,63.5209,1.37521,1.00512,1.14473,0.75871,0.76707,0.85137,0.51276,1.45374,1.14832,1.33055,0.00191542,0.00191542,0.00191542
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yolo_training/NAO_detector9/weights/best_openvino_model/metadata.yaml

@@ -0,0 +1,22 @@
+description: Ultralytics best model trained on yolo_dataset/dataset.yaml
+author: Ultralytics
+date: '2025-07-31T01:20:11.832379'
+version: 8.3.154
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 320
+- 320
+names:
+  0: NAO-Roboter
+args:
+  batch: 1
+  fraction: 1.0
+  half: false
+  int8: false
+  dynamic: false
+  nms: false
+channels: 3

yolo_training/NAO_detector9/weights/best_saved_model/fingerprint.pb

@@ -0,0 +1 @@
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:5233161973706285970

yolo_training/NAO_detector9/weights/best_saved_model/metadata.yaml

@@ -0,0 +1,21 @@
+description: Ultralytics best model trained on yolo_dataset/dataset.yaml
+author: Ultralytics
+date: '2025-07-31T01:21:01.673821'
+version: 8.3.154
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 320
+- 320
+names:
+  0: NAO-Roboter
+args:
+  batch: 1
+  fraction: 1.0
+  half: false
+  int8: false
+  nms: false
+channels: 3