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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) |