- Use Kaggle notebook to train
- Google Landmark Recognition 2021
- Kaggle
- Data Amount : 1580470
- Class of Data : 81313
- 81313 Class choose the most amount of 30000 classes.
- Each class choose 10 images ( random ) => 300000 Images
- Advantage : Data Balance.
- Disadvantage : Not Enough Data.
- Batch Size: 64
- Resize to image : [64, 64]
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EfficientNet – B7
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Parameters : 140,616,960
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Loss : CrossEntropyLoss
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Learning Rate : 1e-3
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Optimizer : radam
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Epoch : 20
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ResNet 50
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Parameters : 55,587,032
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Loss : CrossEntropyLoss
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Learning Rate : 1e-3
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Optimizer : radam
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Epoch : 20
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N is the total number of predictions returned by the system, across all queries
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M is the total number of queries with at least one sample from the training set visible in it (note that some queries may not depict samples)
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P(i) is the precision at rank i. (example: consider rank 3 - we have already made 3 predictions, and 2 of them are correct. Then P(3) will be 2/3)
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rel(i) denotes the relevance of prediciton i: it’s 1 if the i-th prediction is correct, and 0 otherwise
- Because of the training limit, we can’t train too many data and train more bigger model.
- EfficientNet is more better than ResNet 50, But EfficientNet is more bigger than ResNet 50
- The score in Leaderboard is not good.
- We should think how to train more class and keep the data balanced
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