kunhsiang/NYCU_ML_2025_ImageClassification_ConvNeXtV2_Base

NYCU_ML_2025_ImageClassification

Model Description

This is a convnextv2_base.fcmae_ft_in22k_in1k (2023 - 推薦首選, timm) model fine-tuned for Simpsons character classification.

• Developed by: NYCU ML Course 2025
• Model type: Image Classification
• Framework: PyTorch + timm
• Best Validation Accuracy: 0.9934

Training Details

Hyperparameters

Parameter	Value
Image Resolution	256
Batch Size	80
Learning Rate	0.0001
Optimizer	AdamW
Weight Decay	0.01
Scheduler	CosineAnnealingLR
Label Smoothing	0.1
Epochs	15
CutMix	False
HEM-TA	False

Dataset

• Number of Classes: 50
• Training Samples: 87236
• Validation Samples: 9693

Classes

abraham_grampa_simpson, agnes_skinner, apu_nahasapeemapetilon, barney_gumble, bart_simpson, brandine_spuckler, carl_carlson, charles_montgomery_burns, chief_wiggum, cletus_spuckler, comic_book_guy, disco_stu, dolph_starbeam, duff_man, edna_krabappel, fat_tony, gary_chalmers, gil, groundskeeper_willie, homer_simpson...

Usage

import torch
import timm
from PIL import Image
from torchvision import transforms

# Load model
model = timm.create_model('convnextv2_base.fcmae_ft_in22k_in1k',
                          pretrained=False,
                          num_classes=50)
model.load_state_dict(torch.load('pytorch_model.pth', map_location='cpu'))
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Resize(294),
    transforms.CenterCrop(256),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Predict
img = Image.open('your_image.jpg').convert('RGB')
input_tensor = transform(img).unsqueeze(0)
with torch.no_grad():
    output = model(input_tensor)
    pred = output.argmax(dim=1).item()

License

MIT License