🚀 swin-tiny-patch4-window7-224-finetuned-new_dataset_50e
該模型是 microsoft/swin-tiny-patch4-window7-224 在 imagefolder 數據集上的微調版本。它在評估集上取得了以下結果：
📚 詳細文檔
模型信息
| 屬性 |
詳情 |
| 模型類型 |
swin-tiny-patch4-window7-224-finetuned-new_dataset_50e |
| 訓練數據集 |
imagefolder |
| 評估指標 |
準確率 |
| 評估集損失值 |
0.6407 |
| 評估集準確率 |
0.7973 |
訓練過程
訓練超參數
訓練期間使用了以下超參數：
- 學習率：5e-05
- 訓練批次大小：32
- 評估批次大小：32
- 隨機種子：42
- 梯度累積步數：4
- 總訓練批次大小：128
- 優化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
- 學習率調度器類型：線性
- 學習率調度器熱身比例：0.1
- 訓練輪數：50
訓練結果
| 訓練損失 |
輪數 |
步數 |
驗證損失 |
準確率 |
| 無記錄 |
0.94 |
4 |
0.7081 |
0.6081 |
| 無記錄 |
1.94 |
8 |
0.7104 |
0.6351 |
| 0.5516 |
2.94 |
12 |
0.6911 |
0.6351 |
| 0.5516 |
3.94 |
16 |
0.7156 |
0.7027 |
| 0.537 |
4.94 |
20 |
0.7345 |
0.7297 |
| 0.537 |
5.94 |
24 |
0.6745 |
0.6892 |
| 0.537 |
6.94 |
28 |
0.7146 |
0.7297 |
| 0.5333 |
7.94 |
32 |
0.7057 |
0.6892 |
| 0.5333 |
8.94 |
36 |
0.6531 |
0.7027 |
| 0.4871 |
9.94 |
40 |
0.6405 |
0.7027 |
| 0.4871 |
10.94 |
44 |
0.6126 |
0.6892 |
| 0.4871 |
11.94 |
48 |
0.6303 |
0.7027 |
| 0.4432 |
12.94 |
52 |
0.6264 |
0.7027 |
| 0.4432 |
13.94 |
56 |
0.6347 |
0.7432 |
| 0.3669 |
14.94 |
60 |
0.6698 |
0.6622 |
| 0.3669 |
15.94 |
64 |
0.6346 |
0.7568 |
| 0.3669 |
16.94 |
68 |
0.6510 |
0.6892 |
| 0.3704 |
17.94 |
72 |
0.6491 |
0.6892 |
| 0.3704 |
18.94 |
76 |
0.5947 |
0.7568 |
| 0.3624 |
19.94 |
80 |
0.6248 |
0.7027 |
| 0.3624 |
20.94 |
84 |
0.6580 |
0.7027 |
| 0.3624 |
21.94 |
88 |
0.6345 |
0.7162 |
| 0.3164 |
22.94 |
92 |
0.6092 |
0.7568 |
| 0.3164 |
23.94 |
96 |
0.6498 |
0.7162 |
| 0.2777 |
24.94 |
100 |
0.6915 |
0.7703 |
| 0.2777 |
25.94 |
104 |
0.6482 |
0.7838 |
| 0.2777 |
26.94 |
108 |
0.6407 |
0.7973 |
| 0.2946 |
27.94 |
112 |
0.6135 |
0.7838 |
| 0.2946 |
28.94 |
116 |
0.6819 |
0.7568 |
| 0.2546 |
29.94 |
120 |
0.6401 |
0.7568 |
| 0.2546 |
30.94 |
124 |
0.6370 |
0.7432 |
| 0.2546 |
31.94 |
128 |
0.6488 |
0.7703 |
| 0.2477 |
32.94 |
132 |
0.6429 |
0.7973 |
| 0.2477 |
33.94 |
136 |
0.6540 |
0.7703 |
| 0.1968 |
34.94 |
140 |
0.5895 |
0.7973 |
| 0.1968 |
35.94 |
144 |
0.6242 |
0.7568 |
| 0.1968 |
36.94 |
148 |
0.6575 |
0.7568 |
| 0.2235 |
37.94 |
152 |
0.6263 |
0.7703 |
| 0.2235 |
38.94 |
156 |
0.6225 |
0.7838 |
| 0.2005 |
39.94 |
160 |
0.6731 |
0.7703 |
| 0.2005 |
40.94 |
164 |
0.6844 |
0.7703 |
| 0.2005 |
41.94 |
168 |
0.6550 |
0.7703 |
| 0.2062 |
42.94 |
172 |
0.6700 |
0.7703 |
| 0.2062 |
43.94 |
176 |
0.6661 |
0.7703 |
| 0.1933 |
44.94 |
180 |
0.6606 |
0.7838 |
| 0.1933 |
45.94 |
184 |
0.6757 |
0.7703 |
| 0.1933 |
46.94 |
188 |
0.6889 |
0.7568 |
| 0.1895 |
47.94 |
192 |
0.6940 |
0.7568 |
| 0.1895 |
48.94 |
196 |
0.6919 |
0.7568 |
| 0.1666 |
49.94 |
200 |
0.6899 |
0.7432 |
框架版本
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
📄 許可證
本模型採用 Apache-2.0 許可證。
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