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HBEKS/CIFAR10-custom-cnn

sourceHugging Facemitupdated 4mo agoView on Hugging Face

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Model Card

Custom CNN for CIFAR-10

Model Description

Model ini adalah arsitektur Convolutional Neural Network (CNN) kustom yang dilatih dari awal pada dataset CIFAR-10 untuk tugas klasifikasi gambar menjadi 10 kelas berbeda.

Arsitektur model terdiri dari:

—5 convolutional blocks dengan Batch Normalization
—Global Average Pooling
—2 Fully Connected layers dengan Dropout (0.5 dan 0.3)

10 Kelas yang Dikenali

ID	Kelas
0	airplane
1	automobile
2	bird
3	cat
4	deer
5	dog
6	frog
7	horse
8	ship
9	truck

Performa Model

Set	Akurasi
Train	82.75%
Validation	80.73%
Test	84.78%

Detail Training

Parameter	Nilai
Dataset	CIFAR-10 (50,000 training, 10,000 test)
Epochs	50 (dengan early stopping, patience=8)
Batch Size	64
Optimizer	Adam (lr=0.001, weight_decay=5e-4)
Loss Function	CrossEntropyLoss
Learning Rate Scheduler	ReduceLROnPlateau (patience=3, factor=0.5)
Best Model Epoch	Epoch 49 (Val Acc: 80.73%)

Data Augmentasi

—RandomHorizontalFlip (p=0.5)
—RandomRotation (20 derajat)
—ColorJitter (brightness=0.3, contrast=0.3, saturation=0.3)
—RandomAffine (translate=0.15)

Hasil Pengujian

Metrik	Nilai
Test Accuracy	84.78%
Correct Predictions	8,478 / 10,000
Test Loss	0.4654

Penulis

—Nama: [Daniel Wuliutomo]
—Batch: batch-11