许可证：apache-2.0
基础模型：facebook/convnextv2-large-22k-224
标签：
- generated_from_trainer
指标：
- accuracy
模型索引：
- 名称：ConvNextV2-large-DogBreed
结果：[]
ConvNextV2-large-DogBreed
该模型是基于facebook/convnextv2-large-22k-224在狗品种分类数据集上微调的版本。
在评估集上取得了以下结果：
模型描述
需补充更多信息
用途与限制
需补充更多信息
训练与评估数据
需补充更多信息
训练过程
训练超参数
训练过程中使用了以下超参数：
- 学习率：1e-05
- 训练批次大小：16
- 评估批次大小：8
- 随机种子：42
- 梯度累积步数：32
- 总训练批次大小：512
- 优化器：Adam（beta1=0.9，beta2=0.999，epsilon=1e-08）
- 学习率调度器类型：线性
- 训练轮次：50
训练结果
| 训练损失 |
轮次 |
步数 |
验证损失 |
准确率 |
| 4.8578 |
1.0 |
13 |
4.6940 |
0.0671 |
| 4.6332 |
1.99 |
26 |
4.4169 |
0.1784 |
| 4.4095 |
2.99 |
39 |
4.1105 |
0.3485 |
| 3.8841 |
3.98 |
52 |
3.7581 |
0.5198 |
| 3.5964 |
4.98 |
65 |
3.3647 |
0.6647 |
| 3.2781 |
5.97 |
78 |
2.9442 |
0.7677 |
| 2.6006 |
6.97 |
91 |
2.5252 |
0.8180 |
| 2.2638 |
7.96 |
104 |
2.1256 |
0.8467 |
| 1.9609 |
8.96 |
117 |
1.7626 |
0.8766 |
| 1.3962 |
9.95 |
130 |
1.4453 |
0.9042 |
| 1.143 |
10.95 |
143 |
1.1818 |
0.9102 |
| 0.9423 |
11.94 |
156 |
0.9697 |
0.9138 |
| 0.7674 |
12.94 |
169 |
0.8097 |
0.9174 |
| 0.5007 |
13.93 |
182 |
0.6922 |
0.9186 |
| 0.4097 |
14.93 |
195 |
0.5999 |
0.9162 |
| 0.3392 |
16.0 |
209 |
0.5174 |
0.9269 |
| 0.2285 |
17.0 |
222 |
0.4685 |
0.9257 |
| 0.184 |
17.99 |
235 |
0.4337 |
0.9210 |
| 0.1587 |
18.99 |
248 |
0.4058 |
0.9257 |
| 0.1112 |
19.98 |
261 |
0.3824 |
0.9222 |
| 0.0967 |
20.98 |
274 |
0.3712 |
0.9150 |
| 0.0838 |
21.97 |
287 |
0.3584 |
0.9186 |
| 0.0665 |
22.97 |
300 |
0.3468 |
0.9174 |
| 0.0589 |
23.96 |
313 |
0.3428 |
0.9186 |
| 0.0551 |
24.96 |
326 |
0.3364 |
0.9186 |
| 0.0512 |
25.95 |
339 |
0.3334 |
0.9162 |
| 0.0441 |
26.95 |
352 |
0.3278 |
0.9210 |
| 0.0428 |
27.94 |
365 |
0.3275 |
0.9150 |
| 0.0387 |
28.94 |
378 |
0.3237 |
0.9210 |
| 0.036 |
29.93 |
391 |
0.3242 |
0.9150 |
| 0.0337 |
30.93 |
404 |
0.3204 |
0.9186 |
| 0.0328 |
32.0 |
418 |
0.3176 |
0.9198 |
| 0.0304 |
33.0 |
431 |
0.3183 |
0.9162 |
| 0.0283 |
33.99 |
444 |
0.3150 |
0.9210 |
| 0.029 |
34.99 |
457 |
0.3168 |
0.9174 |
| 0.0264 |
35.98 |
470 |
0.3146 |
0.9174 |
| 0.0259 |
36.98 |
483 |
0.3162 |
0.9174 |
| 0.0258 |
37.97 |
496 |
0.3126 |
0.9186 |
| 0.0251 |
38.97 |
509 |
0.3131 |
0.9174 |
| 0.0239 |
39.96 |
522 |
0.3145 |
0.9186 |
| 0.0234 |
40.96 |
535 |
0.3120 |
0.9198 |
| 0.023 |
41.95 |
548 |
0.3102 |
0.9198 |
| 0.0226 |
42.95 |
561 |
0.3123 |
0.9198 |
| 0.0222 |
43.94 |
574 |
0.3140 |
0.9186 |
| 0.0225 |
44.94 |
587 |
0.3119 |
0.9186 |
| 0.0215 |
45.93 |
600 |
0.3106 |
0.9198 |
| 0.0209 |
46.93 |
613 |
0.3113 |
0.9198 |
| 0.0212 |
48.0 |
627 |
0.3115 |
0.9198 |
| 0.021 |
49.0 |
640 |
0.3113 |
0.9198 |
| 0.0212 |
49.76 |
650 |
0.3113 |
0.9198 |
框架版本
- Transformers 4.34.1
- PyTorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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