🚀 clip-finetuned-csu-p14-336-e3l57-l
該模型是 openai/clip-vit-large-patch14-336 在未知數據集上的微調版本，在評估集上損失為 0.4700。
🚀 快速開始
本模型是 openai/clip-vit-large-patch14-336 在未知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
模型描述
更多信息待補充。
預期用途和限制
更多信息待補充。
訓練和評估數據
更多信息待補充。
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-07
- 訓練批次大小：128
- 評估批次大小：8
- 隨機種子：42
- 優化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
- 學習率調度器類型：線性
- 訓練輪數：3.0
訓練結果
| 訓練損失 |
輪數 |
步數 |
驗證損失 |
| 0.3812 |
0.0533 |
500 |
1.1163 |
| 0.2683 |
0.1067 |
1000 |
0.9684 |
| 0.2119 |
0.1600 |
1500 |
0.9100 |
| 0.1889 |
0.2133 |
2000 |
0.8620 |
| 0.2071 |
0.2666 |
2500 |
0.7918 |
| 0.1588 |
0.3200 |
3000 |
0.7657 |
| 0.1718 |
0.3733 |
3500 |
0.7610 |
| 0.1113 |
0.4266 |
4000 |
0.7458 |
| 0.1313 |
0.4799 |
4500 |
0.7168 |
| 0.1649 |
0.5333 |
5000 |
0.7019 |
| 0.1245 |
0.5866 |
5500 |
0.6812 |
| 0.1286 |
0.6399 |
6000 |
0.6502 |
| 0.1076 |
0.6933 |
6500 |
0.6154 |
| 0.1477 |
0.7466 |
7000 |
0.6118 |
| 0.1315 |
0.7999 |
7500 |
0.6016 |
| 0.1413 |
0.8532 |
8000 |
0.5849 |
| 0.124 |
0.9066 |
8500 |
0.5766 |
| 0.1215 |
0.9599 |
9000 |
0.5559 |
| 0.131 |
1.0132 |
9500 |
0.5633 |
| 0.0348 |
1.0666 |
10000 |
0.5531 |
| 0.0687 |
1.1199 |
10500 |
0.5495 |
| 0.0749 |
1.1732 |
11000 |
0.5474 |
| 0.1083 |
1.2265 |
11500 |
0.5416 |
| 0.0485 |
1.2799 |
12000 |
0.5412 |
| 0.0449 |
1.3332 |
12500 |
0.5511 |
| 0.0847 |
1.3865 |
13000 |
0.5492 |
| 0.0702 |
1.4398 |
13500 |
0.5509 |
| 0.0487 |
1.4932 |
14000 |
0.5447 |
| 0.072 |
1.5465 |
14500 |
0.5490 |
| 0.0325 |
1.5998 |
15000 |
0.5443 |
| 0.0894 |
1.6532 |
15500 |
0.5264 |
| 0.0503 |
1.7065 |
16000 |
0.5209 |
| 0.0568 |
1.7598 |
16500 |
0.5083 |
| 0.0589 |
1.8131 |
17000 |
0.5093 |
| 0.0892 |
1.8665 |
17500 |
0.4983 |
| 0.0584 |
1.9198 |
18000 |
0.4886 |
| 0.063 |
1.9731 |
18500 |
0.4945 |
| 0.0493 |
2.0265 |
19000 |
0.4956 |
| 0.0246 |
2.0798 |
19500 |
0.4871 |
| 0.0385 |
2.1331 |
20000 |
0.4830 |
| 0.0574 |
2.1864 |
20500 |
0.4849 |
| 0.039 |
2.2398 |
21000 |
0.4872 |
| 0.0653 |
2.2931 |
21500 |
0.4838 |
| 0.0325 |
2.3464 |
22000 |
0.4876 |
| 0.0578 |
2.3997 |
22500 |
0.4870 |
| 0.039 |
2.4531 |
23000 |
0.4805 |
| 0.0536 |
2.5064 |
23500 |
0.4824 |
| 0.0382 |
2.5597 |
24000 |
0.4809 |
| 0.0479 |
2.6131 |
24500 |
0.4749 |
| 0.0268 |
2.6664 |
25000 |
0.4723 |
| 0.0406 |
2.7197 |
25500 |
0.4743 |
| 0.0349 |
2.7730 |
26000 |
0.4718 |
| 0.017 |
2.8264 |
26500 |
0.4721 |
| 0.0286 |
2.8797 |
27000 |
0.4709 |
| 0.0265 |
2.9330 |
27500 |
0.4708 |
| 0.0552 |
2.9863 |
28000 |
0.4700 |
框架版本
- Transformers 4.45.0.dev0
- Pytorch 1.12.1
- Datasets 2.21.0
- Tokenizers 0.19.1
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