feat: implement comprehensive land cover classification pipeline with model benchmarking and experiment logging

2026-07-17 18:54:25 +07:00
parent a258db54cd
commit abab846884
69 changed files with 155558 additions and 105 deletions
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+🚀 BẮT ĐẦU TÌM KIẾM SIÊU THAM SỐ CHO SWIN-UNET
+Loading data from dataset_cache/training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
+Using device: cuda
+
+[1/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.5984
+🌟 NEW BEST ACCURACY: 0.5984
+
+[2/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.6063
+🌟 NEW BEST ACCURACY: 0.6063
+
+[3/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6142
+🌟 NEW BEST ACCURACY: 0.6142
+
+[4/48] Training with params: {'embed_dim': 64, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6772
+🌟 NEW BEST ACCURACY: 0.6772
+
+[5/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.5827
+
+[6/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.5984
+
+[7/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.5669
+
+[8/48] Training with params: {'embed_dim': 64, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6142
+
+[9/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.6142
+
+[10/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.5118
+
+[11/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.5591
+
+[12/48] Training with params: {'embed_dim': 64, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6063
+
+[13/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.7008
+🌟 NEW BEST ACCURACY: 0.7008
+
+[14/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.6142
+
+[15/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6772
+
+[16/48] Training with params: {'embed_dim': 128, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6063
+
+[17/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.5906
+
+[18/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.6457
+
+[19/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.5906
+
+[20/48] Training with params: {'embed_dim': 128, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.5906
+
+[21/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.6220
+
+[22/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.6457
+
+[23/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.5984
+
+[24/48] Training with params: {'embed_dim': 128, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6063
+
+[25/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.7087
+🌟 NEW BEST ACCURACY: 0.7087
+
+[26/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.7323
+🌟 NEW BEST ACCURACY: 0.7323
+
+[27/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6457
+
+[28/48] Training with params: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6142
+
+[29/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.5984
+
+[30/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.5906
+
+[31/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6142
+
+[32/48] Training with params: {'embed_dim': 256, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.7008
+
+[33/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.6299
+
+[34/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.6299
+
+[35/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6378
+
+[36/48] Training with params: {'embed_dim': 256, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6457
+
+[37/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.6142
+
+[38/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.6457
+
+[39/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6378
+
+[40/48] Training with params: {'embed_dim': 512, 'lr': 0.001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6299
+
+[41/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.6378
+
+[42/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.6220
+
+[43/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6142
+
+[44/48] Training with params: {'embed_dim': 512, 'lr': 0.0005, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.7008
+
+[45/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 200}
+Test Accuracy: 0.6457
+
+[46/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.01, 'epochs': 500}
+Test Accuracy: 0.7323
+
+[47/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 200}
+Test Accuracy: 0.6693
+
+[48/48] Training with params: {'embed_dim': 512, 'lr': 0.0001, 'weight_decay': 0.001, 'epochs': 500}
+Test Accuracy: 0.6457
+
+✅ Đã lưu mô hình tốt nhất (Acc: 0.7323) vào land_classification_model/model_swin-unet_optimized_95.joblib
+Cấu hình tốt nhất: {'embed_dim': 256, 'lr': 0.001, 'weight_decay': 0.01, 'epochs': 500}