Files
remote-sensing/.temp/log_train_2d_temporal_pretrained.txt

32 lines
7.5 KiB
Plaintext

🚀 BẮT ĐẦU PIPELINE 2D PATCH-BASED & CLOUD REMOVAL (TEMPORAL 24-CHANNELS)
Loading 2D patches from dataset_cache/training_data_2d_temporal.joblib...
Training 2D CNN with Data Augmentation... Dataset shape: (706, 24, 16, 16)
Using device: cuda
Downloading: "https://download.pytorch.org/models/swin_t-704ceda3.pth" to /home/x79/.cache/torch/hub/checkpoints/swin_t-704ceda3.pth
0%| | 0.00/108M [00:00<?, ?B/s]
1%| | 768k/108M [00:00<00:15, 7.14MB/s]
2%|▏ | 1.88M/108M [00:00<00:12, 8.96MB/s]
3%|▎ | 3.12M/108M [00:00<00:10, 10.7MB/s]
4%|▍ | 4.25M/108M [00:00<00:09, 11.0MB/s]
5%|▌ | 5.50M/108M [00:00<00:09, 11.3MB/s]
6%|▌ | 6.62M/108M [00:00<00:09, 11.4MB/s]
7%|▋ | 7.75M/108M [00:00<00:09, 11.5MB/s]
8%|▊ | 8.88M/108M [00:00<00:09, 11.5MB/s]
9%|▉ | 10.0M/108M [00:00<00:08, 11.6MB/s]
10%|█ | 11.1M/108M [00:01<00:08, 11.6MB/s]
11%|█▏ | 12.2M/108M [00:01<00:08, 11.7MB/s]
12%|█▏ | 13.4M/108M [00:01<00:08, 11.6MB/s]
13%|█▎ | 14.5M/108M [00:01<00:08, 11.7MB/s]
14%|█▍ | 15.6M/108M [00:01<00:08, 11.7MB/s]
15%|█▌ | 16.8M/108M [00:01<00:08, 11.7MB/s]
17%|█▋ | 18.0M/108M [00:01<00:08, 11.8MB/s]
18%|█▊ | 19.1M/108M [00:01<00:07, 11.7MB/s]
19%|█▊ | 20.2M/108M [00:01<00:07, 11.6MB/s]
20%|█▉ | 21.5M/108M [00:01<00:07, 11.7MB/s]
21%|██ | 22.8M/108M [00:02<00:07, 11.7MB/s]
22%|██▏ | 24.0M/108M [00:02<00:07, 11.8MB/s]
23%|██▎ | 25.1M/108M [00:02<00:07, 11.7MB/s]
24%|██▍ | 26.4M/108M [00:02<00:07, 11.8MB/s]
25%|██▌ | 27.5M/108M [00:02<00:07, 11.7MB/s]
26%|██▋ | 28.6M/108M [00:02<00:07, 11.8MB/s]
28%|██▊ | 29.9M/108M [00:02<00:06, 11.8MB/s]
29%|██▉ | 31.1M/108M [00:02<00:06, 11.7MB/s]
30%|██▉ | 32.2M/108M [00:02<00:06, 11.6MB/s]
31%|███ | 33.5M/108M [00:03<00:06, 11.7MB/s]
32%|███▏ | 34.8M/108M [00:03<00:06, 11.8MB/s]
33%|███▎ | 36.0M/108M [00:03<00:06, 11.8MB/s]
34%|███▍ | 37.1M/108M [00:03<00:06, 11.6MB/s]
35%|███▌ | 38.2M/108M [00:03<00:06, 11.6MB/s]
36%|███▋ | 39.4M/108M [00:03<00:06, 11.7MB/s]
37%|███▋ | 40.5M/108M [00:03<00:06, 11.6MB/s]
38%|███▊ | 41.6M/108M [00:03<00:06, 11.6MB/s]
40%|███▉ | 42.8M/108M [00:03<00:05, 11.5MB/s]
41%|████ | 43.9M/108M [00:03<00:05, 11.5MB/s]
42%|████▏ | 45.0M/108M [00:04<00:05, 11.5MB/s]
43%|████▎ | 46.1M/108M [00:04<00:05, 11.5MB/s]
44%|████▎ | 47.2M/108M [00:04<00:05, 11.6MB/s]
45%|████▍ | 48.4M/108M [00:04<00:05, 11.5MB/s]
46%|████▌ | 49.5M/108M [00:04<00:05, 11.5MB/s]
47%|████▋ | 50.6M/108M [00:04<00:05, 11.5MB/s]
48%|████▊ | 51.8M/108M [00:04<00:05, 11.5MB/s]
49%|████▉ | 52.9M/108M [00:04<00:05, 11.6MB/s]
50%|████▉ | 54.0M/108M [00:04<00:04, 11.5MB/s]
51%|█████ | 55.1M/108M [00:05<00:04, 11.3MB/s]
52%|█████▏ | 56.2M/108M [00:05<00:04, 11.2MB/s]
53%|█████▎ | 57.4M/108M [00:05<00:05, 10.4MB/s]
54%|█████▍ | 58.5M/108M [00:06<00:13, 3.73MB/s]
55%|█████▍ | 59.5M/108M [00:06<00:11, 4.51MB/s]
56%|█████▌ | 60.6M/108M [00:06<00:08, 5.55MB/s]
57%|█████▋ | 61.8M/108M [00:06<00:07, 6.57MB/s]
58%|█████▊ | 62.9M/108M [00:06<00:06, 7.53MB/s]
59%|█████▉ | 64.0M/108M [00:06<00:05, 8.44MB/s]
60%|██████ | 65.1M/108M [00:06<00:04, 9.20MB/s]
61%|██████ | 66.2M/108M [00:06<00:04, 9.82MB/s]
62%|██████▏ | 67.4M/108M [00:06<00:04, 10.3MB/s]
63%|██████▎ | 68.5M/108M [00:06<00:03, 10.7MB/s]
64%|██████▍ | 69.6M/108M [00:07<00:03, 10.9MB/s]
65%|██████▌ | 70.8M/108M [00:07<00:03, 11.1MB/s]
66%|██████▋ | 71.9M/108M [00:07<00:03, 11.2MB/s]
67%|██████▋ | 73.0M/108M [00:07<00:03, 11.3MB/s]
69%|██████▊ | 74.1M/108M [00:07<00:03, 11.3MB/s]
70%|██████▉ | 75.2M/108M [00:07<00:03, 11.4MB/s]
71%|███████ | 76.4M/108M [00:07<00:02, 11.4MB/s]
72%|███████▏ | 77.5M/108M [00:07<00:02, 11.5MB/s]
73%|███████▎ | 78.8M/108M [00:07<00:02, 11.6MB/s]
74%|███████▍ | 80.0M/108M [00:08<00:02, 11.7MB/s]
75%|███████▍ | 81.1M/108M [00:08<00:02, 11.7MB/s]
76%|███████▌ | 82.4M/108M [00:08<00:02, 11.7MB/s]
77%|███████▋ | 83.5M/108M [00:08<00:02, 11.7MB/s]
78%|███████▊ | 84.6M/108M [00:08<00:02, 11.2MB/s]
79%|███████▉ | 86.0M/108M [00:08<00:01, 11.9MB/s]
81%|████████ | 87.2M/108M [00:08<00:01, 11.8MB/s]
82%|████████▏ | 88.5M/108M [00:08<00:01, 11.8MB/s]
83%|████████▎ | 89.6M/108M [00:08<00:01, 11.7MB/s]
84%|████████▍ | 90.8M/108M [00:08<00:01, 11.6MB/s]
85%|████████▍ | 91.9M/108M [00:09<00:01, 11.6MB/s]
86%|████████▌ | 93.0M/108M [00:09<00:01, 11.5MB/s]
87%|████████▋ | 94.1M/108M [00:09<00:01, 11.5MB/s]
88%|████████▊ | 95.2M/108M [00:09<00:01, 11.5MB/s]
89%|████████▉ | 96.4M/108M [00:09<00:01, 11.5MB/s]
90%|█████████ | 97.5M/108M [00:09<00:00, 11.5MB/s]
91%|█████████ | 98.6M/108M [00:09<00:00, 11.4MB/s]
92%|█████████▏| 99.8M/108M [00:09<00:00, 11.4MB/s]
93%|█████████▎| 101M/108M [00:09<00:00, 11.4MB/s]
94%|█████████▍| 102M/108M [00:10<00:00, 11.5MB/s]
95%|█████████▌| 103M/108M [00:10<00:00, 11.5MB/s]
96%|█████████▋| 104M/108M [00:10<00:00, 11.4MB/s]
97%|█████████▋| 105M/108M [00:10<00:00, 11.4MB/s]
98%|█████████▊| 106M/108M [00:10<00:00, 11.5MB/s]
99%|█████████▉| 108M/108M [00:10<00:00, 7.44MB/s]
100%|██████████| 108M/108M [00:10<00:00, 10.5MB/s]
Epoch 1/150 - Loss: 2.2017 - Test Acc: 0.2535 🌟
Epoch 2/150 - Loss: 1.8870 - Test Acc: 0.4225 🌟
Epoch 4/150 - Loss: 1.6594 - Test Acc: 0.5704 🌟
Epoch 6/150 - Loss: 1.4675 - Test Acc: 0.6761 🌟
Epoch 10/150 - Loss: 1.3412 - Test Acc: 0.6549
Epoch 14/150 - Loss: 1.2262 - Test Acc: 0.7042 🌟
Epoch 17/150 - Loss: 1.1685 - Test Acc: 0.7817 🌟
Epoch 20/150 - Loss: 1.1836 - Test Acc: 0.6690
Epoch 30/150 - Loss: 1.0356 - Test Acc: 0.7676
Epoch 38/150 - Loss: 0.9285 - Test Acc: 0.7887 🌟
Epoch 40/150 - Loss: 0.9587 - Test Acc: 0.8099 🌟
Epoch 50/150 - Loss: 0.8417 - Test Acc: 0.7887
Epoch 60/150 - Loss: 0.7836 - Test Acc: 0.7676
Epoch 70/150 - Loss: 0.7438 - Test Acc: 0.7817
Epoch 80/150 - Loss: 0.7344 - Test Acc: 0.7958
Epoch 90/150 - Loss: 0.7444 - Test Acc: 0.8028
Epoch 100/150 - Loss: 0.7439 - Test Acc: 0.8099
Epoch 110/150 - Loss: 0.7303 - Test Acc: 0.8099
Epoch 120/150 - Loss: 0.7313 - Test Acc: 0.7887
Epoch 130/150 - Loss: 0.7349 - Test Acc: 0.8028
Epoch 131/150 - Loss: 0.7304 - Test Acc: 0.8380 🌟
Epoch 140/150 - Loss: 0.7594 - Test Acc: 0.7606
Epoch 150/150 - Loss: 0.7580 - Test Acc: 0.7817
✅ Đã lưu mô hình đạt 0.8380 vào land_classification_model/model_cnn_2d_95.joblib
🎉 Hoàn tất quá trình! Check-point với Accuracy > 95% đã được lưu!