---
tags:
- image-classification
- computer-vision
- deep-learning
- vehicle
- pytorch
- convnext
- k-fold
- tta
license: cc-by-nc-sa-4.0
datasets:
- custom
model-index:
- name: Vehicle Model Classifier (ConvNeXt + MixUp/CutMix)
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: Custom Used Car Dataset
      type: image
    metrics:
    - type: log_loss
      value: 0.1435
sdk_version: 5.38.0
---

# Vehicle Model Classifier

This deep learning model classifies **396 real-world used car models** from vehicle images.

## Final Model Details

- **Backbone**: ConvNeXt-Base
- **Framework**: PyTorch
- **Augmentation**: MixUp / CutMix / Albumentations
- **Scheduler**: CosineAnnealingLR / ReduceLROnPlateau
- **Validation**: Stratified K-Fold (3-fold)
- **Ensemble**: Logit & Soft Voting Ensemble
- **TTA**: 3-view & 5-view Test Time Augmentation

## How to Use

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

processor = AutoImageProcessor.from_pretrained("myneighborh/vehicle-model-classifier")
model = AutoModelForImageClassification.from_pretrained("myneighborh/vehicle-model-classifier")

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