result

01.train_ODC_local_with_Mic_supplyer.ipynb

@@ -2073,7 +2073,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "env_01",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2087,7 +2087,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.18"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

model_cnn_land_use_info.json

@@ -0,0 +1,33 @@
+{
+  "model_type": "1D-CNN (PyTorch)",
+  "input_shape": [
+    3,
+    13
+  ],
+  "num_classes": 8,
+  "classes": [
+    "Lua tom",
+    "Lua",
+    "CHN",
+    "CLN",
+    "TS",
+    "Song",
+    "Dat xay dung",
+    "Rung"
+  ],
+  "label_mapping": {
+    "Lua tom": "0",
+    "Lua": "1",
+    "CHN": "2",
+    "CLN": "3",
+    "TS": "4",
+    "Song": "5",
+    "Dat xay dung": "6",
+    "Rung": "7"
+  },
+  "num_parameters": 59848,
+  "accuracy": 0.7123893805309734,
+  "precision": 0.7304290287005406,
+  "recall": 0.7123893805309734,
+  "f1_score": 0.6849467947376894
+}

model_swinunet_land_use_info.json

@@ -0,0 +1,33 @@
+{
+  "model_type": "Swin-UNet (PyTorch)",
+  "input_shape": [
+    39
+  ],
+  "embed_dim": 128,
+  "num_classes": 8,
+  "classes": [
+    "Lua tom",
+    "Lua",
+    "CHN",
+    "CLN",
+    "TS",
+    "Song",
+    "Dat xay dung",
+    "Rung"
+  ],
+  "label_mapping": {
+    "Lua tom": "0",
+    "Lua": "1",
+    "CHN": "2",
+    "CLN": "3",
+    "TS": "4",
+    "Song": "5",
+    "Dat xay dung": "6",
+    "Rung": "7"
+  },
+  "num_parameters": 958536,
+  "accuracy": 0.8539823008849557,
+  "precision": 0.8893103005023856,
+  "recall": 0.8539823008849557,
+  "f1_score": 0.8666980102256664
+}

model_xgboost_info.json

@@ -0,0 +1,31 @@
+{
+  "model_type": "XGBoost",
+  "num_classes": 8,
+  "classes": [
+    "Lua tom",
+    "Lua",
+    "CHN",
+    "CLN",
+    "TS",
+    "Song",
+    "Dat xay dung",
+    "Rung"
+  ],
+  "num_features": 39,
+  "params": {
+    "objective": "multi:softmax",
+    "num_class": 8,
+    "max_depth": 6,
+    "learning_rate": 0.1,
+    "n_estimators": 200,
+    "subsample": 0.8,
+    "colsample_bytree": 0.8,
+    "random_state": 42,
+    "n_jobs": -1,
+    "eval_metric": "mlogloss"
+  },
+  "accuracy": 0.9070796460176991,
+  "precision": 0.9252693488976674,
+  "recall": 0.9070796460176991,
+  "f1_score": 0.9111599574514205
+}