kikogazda/Efficient_NetV2_Edition

🚗 EfficientNetV2 Car Classifier: Fine-Grained Vehicle Recognition

EfficientNetV2 Car Classifier delivers robust, fine-grained recognition for 196 car makes and models, powered by EfficientNetV2, state-of-the-art augmentations, rigorous metric tracking, and full visual explainability with Grad-CAM. Developed by kikogazda, 2025.

📁 Project Structure

<pre> EfficientNetV2Edition/ ├── efficientnetv2bestmodel.pth # Best model weights ├── Lastmodel.ipynb # Full training & evaluation pipeline ├── classmapping.json # Class index to name mapping ├── .csv # Logs, splits, labels, and metrics ├── .png # Visualizations and Grad-CAM outputs ├── README.md # Model card (this file) └── ... # Additional scripts, reports, and assets </pre>

🚦 Table of Contents

• —Overview
• —Dataset & Preprocessing
• —Model Architecture
• —Training Pipeline
• —Explainability (Grad-CAM)
• —Visualizations
• —Metrics & Results
• —Hugging Face & Demo
• —Download Resources
• —Usage & Inference

Overview

EfficientNetV2 Car Classifier tackles the real-world challenge of distinguishing between 196 car makes and models, even when differences are nearly imperceptible. Highlights:

• —Modern EfficientNetV2 backbone with transfer learning
• —Aggressive, real-world augmentation pipeline
• —Class balancing for rare makes/models
• —Extensive, scriptable metric tracking and reporting
• —End-to-end explainability with Grad-CAM
• —Fully reproducible, robust, and deployment-ready

Dataset & Preprocessing

• —Dataset: Stanford Cars 196
• —196 classes, 16,185 images (official train/test split)
• —Detailed make/model/year for each image
• —Preprocessing:
• —Annotation CSV export and class mapping JSON
• —Stratified train/val/test split (maintains class distribution)
• —Outlier cleaning and normalization
• —Augmentations: random resized crop, flip, rotate, color jitter, blur
• —ImageNet mean/std normalization

Model Architecture

• —Backbone: EfficientNetV2 (pretrained)
• —All but the last blocks frozen initially
• —Custom classifier head for 196 classes (Linear → ReLU → Dropout → Linear)
• —Optimization:
• —Adam optimizer
• —Cross-Entropy loss (with label smoothing)
• —Learning rate scheduling (ReduceLROnPlateau)
• —Early stopping (macro F1 on validation)
• —WeightedRandomSampler for class balance

Flow: Input → [Augmentations] → EfficientNetV2 Backbone → Custom Head → Softmax (196 classes)

Training Pipeline

• —Epochs: Up to 25 (early stopping enabled)
• —Batch Size: 32 (weighted sampling)
• —Validation: Macro/micro metrics, confusion matrix, Top-3/Top-5 accuracy
• —Logging: All metrics and losses to CSV, plus high-res visual plots:
• —Accuracy/F1 per epoch
• —Precision/Recall (macro, weighted)
• —Loss curve
• —Top-3/Top-5 accuracy
• —Artifacts: All reports, CSVs, and visuals in repo for transparency

Explainability (Grad-CAM)

Grad-CAM overlays highlight image regions most responsible for model predictions—letting you "see" what the network is using for its decisions.

• —Why? Trust, transparency, debugging.
• —How? For every prediction, a heatmap overlay shows most influential pixels.

[image] Heatmaps visualize key decision regions for each sample.

Visualizations

Here are key visualizations from the training and evaluation process, including loss curves, accuracy plots, and Grad-CAM++ overlays that illustrate what the model focuses on.

🎯 Accuracy & F1 Score per Epoch

Visualizing training and validation accuracy alongside macro F1 score.

[image]

📉 Training vs Validation Loss

Clear comparison of model learning over time.

[image]

📈 Precision & Recall Trends

Macro and weighted precision/recall for detailed class-wise performance.

[image]

📊 Top-3 and Top-5 Accuracy Over Epochs

Measuring how often the correct class is within the top predictions.

[image]

🏆 Top-20 Most Accurate Classes

Sorted bar plot of classes the model predicts with the highest accuracy.

[image]

🧩 Confusion Matrix

High-resolution heatmap showing misclassifications and accuracy by class.

[image]

📈 Metrics & Results

Hugging Face Demo

Live Gradio Demo: Click here to launch the demo

Download Resources

• —Stanford Cars 196 Dataset: Direct download from Stanford
• —Trained Model Weights: Download from Hugging Face (efficientnetv2_best_model.pth)
• —Class mapping/metadata: Included as class_mapping.json in this repo

Usage & Inference

1. Install dependencies

pip install -r requirements.txt
pip install torch torchvision pytorch-grad-cam gradio

import torch
from torchvision import transforms
from PIL import Image
import json
from efficientnet_pytorch import EfficientNet

# Load model
model = EfficientNet.from_pretrained('efficientnet-b2', num_classes=196)
model.load_state_dict(torch.load("efficientnetv2_best_model.pth", map_location="cpu"))
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
img = Image.open("your_image.jpg").convert("RGB")
input_tensor = transform(img).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(input_tensor)
    pred = output.argmax(1).item()

# Class name
with open("class_mapping.json") as f:
    class_map = json.load(f)
print("Predicted class:", class_map[str(pred)])