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world-wonders-classification – AI Model by sandi-irvan | AlphaNeural AI
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Model Training Report
Model Details
Model
: ResNet50
Number of Classes
: 12
Total Epochs
: 15
Optimizer
: Adam
Learning Rate
: 0.001
Loss Function
: CrossEntropyLoss
Training Accuracy per Epoch
Epoch
Training Accuracy (%)
1
58.32
2
63.15
3
69.45
4
74.21
5
77.58
6
80.32
7
82.01
8
83.50
9
84.72
10
85.30
11
86.85
12
87.20
13
88.10
14
89.25
15
90.30
Test Results
Average Test Loss
: 0.1985
Test Accuracy
: 91.76%
Additional Notes
The model achieved 91.76% on the test set, exceeding the 90% target.
Training improvements were consistent, with significant gains in accuracy from epoch 5 onwards.
This was achieved using a
pre-trained ResNet50 model
fine-tuned on the building classification dataset.
Further fine-tuning or dataset expansion might improve the model even more.
the dataset that is used for training is from kaggle :
https://www.kaggle.com/datasets/balabaskar/wonders-of-the-world-image-classification?resource=download
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