README.md

metadata

title: Gradio Car Classification App
emoji: 🚗
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.11.0
python_version: '3.11'
app_file: app.py
pinned: false

Gradio Car Classification App

This app compares 3 image classification approaches on car images:

• Fine-tuned transfer learning model (SqueezeNet) trained on a custom car dataset
• Zero-shot CLIP (openai/clip-vit-base-patch32)
• OpenAI vision model (LLM image classification)

Dataset Used For Training

• Hugging Face dataset loader: load_dataset("tanganke/stanford_cars")
• Original dataset reference: Stanford Cars
• Number of classes used in this project: 12
• Classes: audi, bmw, chevrolet, dodge, ford, honda, hyundai, mercedes-benz, nissan, tesla, toyota, volkswagen

Dataset preparation command:

python3 prepare_car_dataset.py --reset \
  --brands audi bmw chevrolet ford honda hyundai mercedes-benz nissan tesla toyota volkswagen dodge \
  --train-per-brand 120 \
  --test-per-brand 30 \
  --image-size 256

Preprocessing Steps

• Train transform: random resized crop 224x224, random horizontal flip, ImageNet normalization
• Validation/Test transform: resize 256, center crop 224x224, ImageNet normalization
• Inference (app.py): same ImageNet normalization as training/evaluation

Trained Model

• Hugging Face model link: https://huggingface.co/saettsam/car-classification-model
• Local output directory: models/custom_resnet18/
• Architecture: squeezenet1_1

Application Link

• Hugging Face Space link: https://huggingface.co/spaces/saettsam/car-classification-model

Training Performance

Training Loss	Epoch	Validation Loss	Accuracy
2.5523	1	2.3256	0.2208
2.2068	2	2.1537	0.3042
1.9677	3	1.9918	0.3583
1.8091	4	1.8408	0.4125
1.6791	5	1.6751	0.4500
1.5624	6	1.5332	0.5333

Example Image Results

The table below reports the true class and Top-3 predictions for the custom model and CLIP.

Image	True Class	Custom Top-3 (score)	CLIP Top-3 (score)	OpenAI LLM (label, confidence)
audi.jpg	audi	audi (0.2694) ford (0.2654) nissan (0.1171)	audi (0.7856) chevrolet (0.0776) volkswagen (0.0398)	audi (0.9500)
bmw.jpg	bmw	audi (0.5224) bmw (0.2075) ford (0.0745)	bmw (0.9917) hyundai (0.0027) mercedes-benz (0.0021)	bmw (0.9500)
chevrolet.jpg	chevrolet	ford (0.2439) chevrolet (0.2244) volkswagen (0.1553)	chevrolet (0.9625) ford (0.0171) dodge (0.0143)	chevrolet (0.770)
ford.jpg	ford	ford (0.3127) bmw (0.1521) audi (0.1196)	ford (0.6104) chevrolet (0.1770) toyota (0.0921)	ford (0.9700)
honda.jpg	honda	honda (0.8067) toyota (0.0681) bmw (0.0375)	toyota (0.4324) nissan (0.2534) hyundai (0.1611)	honda (0.9300)
hyundai.jpg	hyundai	hyundai (0.4650) audi (0.1100) toyota (0.0825)	hyundai (0.4445) toyota (0.3217) nissan (0.1185)	hyundai (0.800)