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8. 有用なNotebook
10. pytorch lightning実装
[tips] windowsで監視
while true; do nvidia-smi; sleep<second>; done
3. VisionTransformer
使い方: timmから使える
class ViTBase16(nn.Module):
def __init__(self, n_classes, pretrained=False):
super(ViTBase16, self).__init__()
self.model = timm.create_model("vit_base_patch16_224", pretrained=False)
if pretrained:
self.model.load_state_dict(torch.load(MODEL_PATH))
self.model.head = nn.Linear(self.model.head.in_features, n_classes)
def forward(self, x):
x = self.model(x)
return x
notebook: https://www.kaggle.com/abhinand05/vision-transformer-vit-tutorial-baseline
15
19. bi-tempered-logistic-loss
22. stackingの実装
18. アンサンブル
How I got LB 0.901 in a simple way.
I got the LB score of 0.901 by simply using SnapMix augmentation and model ensemble.
Detailed results can be found as below.
Single model result (single fold, no tta, no extra training data)
Resnet50 : LB 0.891 Notebook for training
Model ensemble ( no tta, no extra training data):
Resnet50 (5 folds) : LB 0.897
Resnet50 (5 folds) + Resnet101(5 folds): LB 0.899
Resnet50 (5 folds) + Resnet101(5 folds) + ResNext101(5 folds): LB 0.901
11. clean lab
13. samの実装
9. 前回の手法
5. cyclegan
4. pseud labeling
7. 画像認識モデルSOTA
12. outdrop
17. [方策] ラベルが異なっていると思われる画像を削除
- tf_efficientnet4_nsにおいて、実際健康だが、そうでないと判断した(false Positive)ものを学習から削除
2. 有用なDataAUgmentation
20. ADAMP
adam -> adampでスコア上昇
https://www.kaggle.com/seriousran/adam-vs-adamp-iclr-2021
16. ベンガル語コンペ
[tips] windowsで監視
while true; do nvidia-smi; sleep<second>; done
1. baseline notebook
6. ノイジーラベルに対する対策
21. epochs
resnextとRepVGGは15epoch以上で良さそう
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