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# CIFAR-10 Visual Analyzer 🔍📷

This app classifies uploaded images into one of the 10 CIFAR-10 classes using a fine-tuned ResNet-18 model, and optionally provides a Fourier Transform visualization of RGB channels.

## Model
- Base: ResNet-18
- Trained on: CIFAR-10 (with FFT exploration)
- Accuracy: ~78% (CPU-trained, 12 epochs)

## Features
- **Image Classification** into: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
- **FFT Mode**: Visualize frequency domain per RGB channel

## Usage
1. Upload any image
2. Choose between:
- **Raw**: Classify with the model
- **FFT**: Visualize RGB channel frequency maps

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Built for educational and demonstrative purposes.

cifar10_fouriervision_gradioapp.py

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+# -*- coding: utf-8 -*-
+"""Cifar10-FourierVision-GradioApp.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1uw8cWaCxnSHf2CYhgeF_HYYdMeGP3odV
+"""
+
+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+import torchvision.models as models
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+
+# CIFAR-10 class names
+classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+           'dog', 'frog', 'horse', 'ship', 'truck']
+
+# Load ResNet18 model and adapt final layer for CIFAR-10
+resnet18 = models.resnet18(pretrained=False)
+resnet18.fc = torch.nn.Linear(resnet18.fc.in_features, 10)  # Replace final layer
+resnet18.load_state_dict(torch.load("/content/sample_data/resnet18_fft_cifar10.pth", map_location=torch.device('cpu')))
+resnet18.eval()
+
+# Image transform
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # ResNet18 expects 224x224
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+# FFT Visualizer
+def apply_fft_visualization(image: Image.Image):
+    img_np = np.array(image.resize((32, 32))) / 255.0
+    fft_images = []
+    for i in range(3):
+        channel = img_np[:, :, i]
+        fft = np.fft.fft2(channel)
+        fft_shift = np.fft.fftshift(fft)
+        magnitude = np.log1p(np.abs(fft_shift))
+        fft_images.append(magnitude)
+
+    fig, axs = plt.subplots(1, 3, figsize=(12, 4))
+    for i in range(3):
+        axs[i].imshow(fft_images[i], cmap='inferno')
+        axs[i].set_title(['Red', 'Green', 'Blue'][i])
+        axs[i].axis('off')
+    plt.tight_layout()
+    return fig
+
+# Prediction Function
+def predict(img: Image.Image, mode="Raw"):
+    if mode == "FFT":
+        return None, apply_fft_visualization(img)
+
+    img_tensor = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        outputs = resnet18(img_tensor)
+        _, predicted = torch.max(outputs, 1)
+    label = classes[predicted.item()]
+    return label, None
+
+# Gradio App
+gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Radio(["Raw", "FFT"], label="Mode", value="Raw")
+    ],
+    outputs=[
+        gr.Label(label="Prediction"),
+        gr.Plot(label="FFT Visualization")
+    ],
+    title="CIFAR-10 Visual Analyzer (ResNet18)",
+    description="Upload an image and choose mode: Raw classification (ResNet18) or visualize FFT of RGB channels.\n\nDisclaimer: This model is trained on CIFAR-10 and works best on low-res, centered images."
+).launch()
+

requirements.txt

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+torch
+torchvision
+gradio
+matplotlib
+numpy
+Pillow