Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,87 @@
+---
+license: mit
+tags:
+- image-classification
+- pytorch
+- simpsons
+- convnext
+datasets:
+- custom
+metrics:
+- accuracy
+---
+
+# 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
+
+```python
+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