🚀 organoids - prova_organoid
此模型是 google/vit - base - patch16 - 224 在 imagefolder 數據集上的微調版本。它在評估集上取得了以下成果：
🚀 快速開始
本模型基於 google/vit - base - patch16 - 224 在 imagefolder 數據集上微調得到，可用於圖像分類任務。
📚 詳細文檔
模型描述
該模型是在 imagefolder 數據集上對 google/vit - base - patch16 - 224 進行微調的版本。
預期用途和限制
文檔暫未提供相關信息。
訓練和評估數據
文檔暫未提供相關信息。
訓練過程
訓練超參數
訓練期間使用了以下超參數：
- 學習率：1e - 05
- 訓練批次大小：128
- 評估批次大小：128
- 隨機種子：42
- 梯度累積步數：4
- 總訓練批次大小：512
- 優化器：Adam（β1 = 0.9，β2 = 0.999，ε = 1e - 08）
- 學習率調度器類型：線性
- 學習率調度器預熱比例：0.1
- 訓練輪數：40
訓練結果
| 訓練損失 |
輪數 |
步數 |
驗證損失 |
準確率 |
| 1.2121 |
0.99 |
36 |
1.3066 |
0.4116 |
| 0.8905 |
1.99 |
72 |
0.9344 |
0.6749 |
| 0.6942 |
2.98 |
108 |
0.6875 |
0.7507 |
| 0.6087 |
4.0 |
145 |
0.5493 |
0.7896 |
| 0.5896 |
4.99 |
181 |
0.5028 |
0.7993 |
| 0.6168 |
5.99 |
217 |
0.4787 |
0.8100 |
| 0.5627 |
6.98 |
253 |
0.4373 |
0.8319 |
| 0.5654 |
8.0 |
290 |
0.4324 |
0.8299 |
| 0.5204 |
8.99 |
326 |
0.4130 |
0.8319 |
| 0.5581 |
9.99 |
362 |
0.4264 |
0.8241 |
| 0.5232 |
10.98 |
398 |
0.4074 |
0.8294 |
| 0.483 |
12.0 |
435 |
0.3850 |
0.8445 |
| 0.5208 |
12.99 |
471 |
0.3791 |
0.8489 |
| 0.4937 |
13.99 |
507 |
0.3723 |
0.8528 |
| 0.4436 |
14.98 |
543 |
0.3910 |
0.8440 |
| 0.5169 |
16.0 |
580 |
0.3794 |
0.8465 |
| 0.4394 |
16.99 |
616 |
0.3876 |
0.8440 |
| 0.4616 |
17.99 |
652 |
0.3844 |
0.8465 |
| 0.4983 |
18.98 |
688 |
0.3552 |
0.8591 |
| 0.5295 |
20.0 |
725 |
0.3561 |
0.8547 |
| 0.5121 |
20.99 |
761 |
0.3573 |
0.8537 |
| 0.4379 |
21.99 |
797 |
0.3593 |
0.8576 |
| 0.4653 |
22.98 |
833 |
0.3473 |
0.8601 |
| 0.486 |
24.0 |
870 |
0.3473 |
0.8610 |
| 0.4751 |
24.99 |
906 |
0.3638 |
0.8552 |
| 0.4462 |
25.99 |
942 |
0.3533 |
0.8542 |
| 0.4197 |
26.98 |
978 |
0.3464 |
0.8601 |
| 0.4966 |
28.0 |
1015 |
0.3451 |
0.8649 |
| 0.5004 |
28.99 |
1051 |
0.3634 |
0.8508 |
| 0.4156 |
29.99 |
1087 |
0.3723 |
0.8474 |
| 0.4508 |
30.98 |
1123 |
0.3342 |
0.8669 |
| 0.43 |
32.0 |
1160 |
0.3389 |
0.8639 |
| 0.5004 |
32.99 |
1196 |
0.3416 |
0.8615 |
| 0.4927 |
33.99 |
1232 |
0.3545 |
0.8533 |
| 0.4802 |
34.98 |
1268 |
0.3382 |
0.8610 |
| 0.4334 |
36.0 |
1305 |
0.3480 |
0.8542 |
| 0.4557 |
36.99 |
1341 |
0.3392 |
0.8601 |
| 0.4551 |
37.99 |
1377 |
0.3488 |
0.8542 |
| 0.4643 |
38.98 |
1413 |
0.3424 |
0.8586 |
| 0.513 |
39.72 |
1440 |
0.3433 |
0.8576 |
框架版本
- Transformers 4.28.0
- Pytorch 1.8.1 + cu111
- Datasets 2.14.5
- Tokenizers 0.13.3
📄 許可證
本模型使用 Apache - 2.0 許可證。
信息表格
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