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

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Victor Phan
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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log_swin.txt
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Ignoring read failure while reading: https://sentinel2l2a01.blob.core.windows.net/sentinel2-l2/48/P/XR/2023/03/18/S2A_MSIL2A_20230318T030521_N0510_R075_T48PXR_20240823T234351.SAFE/GRANULE/L2A_T48PXR_A040398_20230318T031920/IMG_DATA/R10m/T48PXR_20230318T030521_B02_10m.tif?st=2026-07-15T12%3A25%3A08Z&se=2026-07-16T13%3A10%3A08Z&sp=rl&sv=2025-07-05&sr=c&skoid=9c8ff44a-6a2c-4dfb-b298-1c9212f64d9a&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2026-07-16T12%3A17%3A04Z&ske=2026-07-23T12%3A17%3A04Z&sks=b&skv=2025-07-05&sig=9xSW/czZHJrJKHHQSNtoTtlu5LlFqC7DKm%2BhTAOOFR8%3D:1
🚀 BẮT ĐẦU HUẤN LUYỆN MÔ HÌNH SWIN-UNET (GPU & CACHE)
Initializing FeatureExtractor (mode=extended)...
📦 Đang load cache: training_data_507bd2ba4ec0d3fe107839cbf73a7a7d.joblib...
✅ Loaded 632 samples từ cache!
⚡ Đã bỏ qua download위성 data (tiết kiệm thời gian)
[CACHE HIT] Using cached dataset with 632 samples
Training SWIN-UNET model...
Building Swin-UNet model on cuda...
[SWIN-UNET] Class distribution: [ 48 89 3 86 74 38 117 50]
[SWIN-UNET] Class weights: [0.37418982 0.20181024 5.98703525 0.20885013 0.24271772 0.47266082
0.15351377 0.35922223]
Training Swin-UNet model with PyTorch (with class weights)...
Swin-UNet Epoch 5/40, Train Loss: 1.4096, Val Loss: 1.2804, Val Acc: 48.03%, LR: 0.000293
[SWIN-UNET] Epoch 5/40 - Train Loss: 1.4096, Val Loss: 1.2804, Val Acc: 48.03%
Swin-UNet Epoch 10/40, Train Loss: 1.1129, Val Loss: 1.2746, Val Acc: 57.48%, LR: 0.000271
[SWIN-UNET] Epoch 10/40 - Train Loss: 1.1129, Val Loss: 1.2746, Val Acc: 57.48%
Swin-UNet Epoch 15/40, Train Loss: 1.0479, Val Loss: 1.3126, Val Acc: 50.39%, LR: 0.000238
[SWIN-UNET] Epoch 15/40 - Train Loss: 1.0479, Val Loss: 1.3126, Val Acc: 50.39%
Swin-UNet Epoch 20/40, Train Loss: 0.9548, Val Loss: 1.1118, Val Acc: 52.76%, LR: 0.000196
[SWIN-UNET] Epoch 20/40 - Train Loss: 0.9548, Val Loss: 1.1118, Val Acc: 52.76%
[SWIN-UNET] Early stopping at epoch 22 (best val loss: 1.1096)
Swin-UNet early stopped at epoch 22
Evaluating model...
Generating classification report...
Saving model...
[MODEL MANAGER] Saving model to: model_train/model_swin-unet_auto.joblib
[MODEL MANAGER] Saving metadata to: model_train/model_swin-unet_auto_info.json
[MODEL MANAGER] Model saved successfully!
Training complete!
✅ Hoàn thành! Accuracy: 0.5354