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yolov11n-cls-noaa-esd-coral-bleaching-classifier_ncnn_model/metadata.yaml

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+description: Ultralytics YOLO11n-cls model trained on V:\OTHER\AI_DATASETS\yolo\datasets\noaa-esd-coral-bleaching-classifierv1\dataset
+author: Ultralytics
+date: '2025-02-21T09:21:25.439789'
+version: 8.3.78
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 1
+task: classify
+batch: 1
+imgsz:
+- 224
+- 224
+names:
+  0: CORAL
+  1: CORAL_BL
+args:
+  batch: 1
+  half: false

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+InnerProduct             linear_39                1 1 140 141 0=2 1=1 2=2560
+Softmax                  softmax_76               1 1 141 out0 0=0 1=1

yolov11n-cls-noaa-esd-coral-bleaching-classifier_ncnn_model/model_ncnn.py

@@ -0,0 +1,26 @@
+import numpy as np
+import ncnn
+import torch
+
+def test_inference():
+    torch.manual_seed(0)
+    in0 = torch.rand(1, 3, 224, 224, dtype=torch.float)
+    out = []
+
+    with ncnn.Net() as net:
+        net.load_param("runs\classify\train\weights\best_ncnn_model\model.ncnn.param")
+        net.load_model("runs\classify\train\weights\best_ncnn_model\model.ncnn.bin")
+
+        with net.create_extractor() as ex:
+            ex.input("in0", ncnn.Mat(in0.squeeze(0).numpy()).clone())
+
+            _, out0 = ex.extract("out0")
+            out.append(torch.from_numpy(np.array(out0)).unsqueeze(0))
+
+    if len(out) == 1:
+        return out[0]
+    else:
+        return tuple(out)
+
+if __name__ == "__main__":
+    print(test_inference())