vietnamese-vit-classification – AI Model by sanvo | AlphaNeural AI

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sanvo/
vietnamese-vit-classification

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vision-transformer

image-classification

scene-classification

google/vit-base-patch16-224

endpoints_compatible

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Vietnamese Scene Classification with Vision Transformer

Fine-tuned google/vit-base-patch16-224 (86M parameters) for Vietnamese scene classification.

Model Details

Architecture: Vision Transformer (ViT-Base, patch size 16, 224×224 input)
Parameters: 86M
Base Model: google/vit-base-patch16-224 (ImageNet-21k pretrained)
Task: Multi-class Vietnamese scene classification
Framework: PyTorch + HuggingFace Transformers

Training

Data Augmentation: RandomResizedCrop, RandomHorizontalFlip, ColorJitter
Optimizer: AdamW (lr=3e-5, weight_decay=0.01)
Scheduler: OneCycleLR with cosine annealing
Gradient Clipping: max_norm=1.0
Validation Accuracy: 94%+

Scene Classes (Vietnamese)

English	Vietnamese
Beach	Bãi biển
City	Thành phố
Forest	Rừng
Mountain	Núi
Rice Field	Ruộng lúa
Market	Chợ
Temple	Chùa
River	Sông

Features

Attention rollout visualization across 12 transformer layers
Per-class precision, recall, and F1 metrics
Formatted confusion matrix output

Usage

python

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image

model = ViTForImageClassification.from_pretrained("sanvo/vietnamese-vit-classification")
processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")

image = Image.open("scene.jpg")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()
print(model.config.id2label[predicted_class])

Links

GitHub: svn05/vietnamese-vit-classification