---
license: apache-2.0
library_name: pytorch
pipeline_tag: image-classification
tags:
- image-classification
- cnn
- cars
- pytorch
datasets:
- tanganke/stanford_cars
metrics:
- accuracy
---

# convnext_tiny-stanford-cars-brand

Model klasifikasi gambar **mobil** hasil fine-tuning **convnext_tiny** (pretrained ImageNet)
pada dataset [tanganke/stanford_cars](https://huggingface.co/datasets/tanganke/stanford_cars).

- Arsitektur   : convnext_tiny (CNN)
- Jumlah kelas : **49** (mode label: `brand`)
- Ukuran input : 288x288
- Epoch        : 8
- **Akurasi test : 92.70%** (baseline kelas mayoritas: 11.11%)

## Cara pakai

```python
import torch, torch.nn as nn
from huggingface_hub import hf_hub_download
from torchvision import transforms
from torchvision.models import resnet50, convnext_tiny
from PIL import Image

path = hf_hub_download("ryosiswand/convnext_tiny-stanford-cars-brand", "model.pth")
ckpt = torch.load(path, map_location="cpu", weights_only=False)

# bangun arsitektur sesuai ckpt["arch"], lalu muat bobotnya
if ckpt["arch"] == "convnext_tiny":
    model = convnext_tiny(weights=None)
    model.classifier[2] = nn.Linear(model.classifier[2].in_features, ckpt["num_classes"])
else:
    model = resnet50(weights=None)
    model.fc = nn.Linear(model.fc.in_features, ckpt["num_classes"])
model.load_state_dict(ckpt["state_dict"])
model.eval()

tf = transforms.Compose([
    transforms.Resize(int(ckpt["img_size"]*1.14)), transforms.CenterCrop(ckpt["img_size"]), transforms.ToTensor(),
    transforms.Normalize(ckpt["mean"], ckpt["std"]),
])
img = Image.open("mobil.jpg").convert("RGB")
probs = model(tf(img).unsqueeze(0)).softmax(-1)[0]
print(ckpt["class_names"][int(probs.argmax())], float(probs.max()))
```

Dibuat sebagai proyek Computer Vision (CNN) - Ruangguru AI Bootcamp.