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metadata

license: apache-2.0
language:
  - en
tags:
  - image-enhancement
  - real-estate
  - photo-enhancement
  - nafnet
  - image-restoration
  - pytorch
  - onnx
  - coreml
  - ios
pipeline_tag: image-to-image
library_name: pytorch
datasets:
  - custom
metrics:
  - psnr
  - ssim
model-index:
  - name: NAFNet Real Estate Enhancement
    results:
      - task:
          type: image-enhancement
          name: Image Enhancement
        metrics:
          - type: psnr
            value: 21.69
            name: PSNR
          - type: ssim
            value: 0.8968
            name: SSIM

NAFNet Real Estate Enhancement

A fine-tuned NAFNet model for enhancing real estate photography. Trained on 577 before/after image pairs to improve lighting, color, and overall image quality.

Model Details

Metric	Value
Architecture	NAFNet (width=32)
Parameters	29.2 million
Model Size	111 MB (FP32) / 56 MB (FP16)
Training Time	5 hours
Training Images	577 pairs
Final PSNR	21.69 dB
Final SSIM	0.8968

Available Formats

Format	File	Size	Use Case
PyTorch	`nafnet_realestate.pth`	117 MB	Training, fine-tuning
ONNX	`nafnet_realestate.onnx`	117 MB	Cross-platform deployment
Core ML	Convert from ONNX	~56 MB	iOS/macOS apps

Performance Benchmarks

Tested on 100 high-resolution real estate images (avg 7.25 megapixels):

Timing

Metric	Value
Average per image	4.0 seconds
Throughput	0.25 images/second
Megapixels/second	1.81 MP/s

Memory Usage

Resource	Usage
RAM	581 MB total
GPU VRAM	8.3 GB peak

Scaling by Resolution

Resolution	RAM	GPU	Time
1080p (2.1 MP)	150-250 MB	~2.5 GB	~1.2s
1440p (3.7 MP)	250-400 MB	~4.3 GB	~2.0s
3K (7.3 MP)	500-800 MB	~8.3 GB	~4.0s
4K (8.3 MP)	600-900 MB	~9.5 GB	~4.6s

Usage

PyTorch

import torch
from PIL import Image
import numpy as np

# Load model
model = NAFNet(img_channel=3, width=32, middle_blk_num=12,
               enc_blk_nums=[2, 2, 4, 8], dec_blk_nums=[2, 2, 2, 2])
checkpoint = torch.load("nafnet_realestate.pth", map_location="cpu")
model.load_state_dict(checkpoint["params"])
model.eval()

# Process image
img = Image.open("input.jpg")
img_tensor = torch.from_numpy(np.array(img)).permute(2, 0, 1).unsqueeze(0).float() / 255.0

with torch.no_grad():
    output = model(img_tensor)

output_img = (output.squeeze(0).permute(1, 2, 0).numpy() * 255).astype(np.uint8)
Image.fromarray(output_img).save("enhanced.jpg")

ONNX Runtime

import onnxruntime as ort
import numpy as np
from PIL import Image

sess = ort.InferenceSession("nafnet_realestate.onnx")
img = np.array(Image.open("input.jpg")).astype(np.float32) / 255.0
img = img.transpose(2, 0, 1)[np.newaxis, ...]

output = sess.run(None, {"input": img})[0]
output_img = (output[0].transpose(1, 2, 0) * 255).astype(np.uint8)
Image.fromarray(output_img).save("enhanced.jpg")

Mobile Deployment (iOS)

All resolutions fit within typical mobile RAM budgets (3-4 GB):

Convert ONNX to Core ML on macOS:

pip install coremltools
python convert_on_mac.py

Add .mlpackage to Xcode project
Use Vision framework for inference

Training

Framework: BasicSR + PyTorch
Base Model: NAFNet-SIDD-width32 (pretrained on denoising)
Loss: L1 + Perceptual (VGG19)
Optimizer: AdamW (lr=1e-3)
Iterations: 12,000

License

Apache 2.0

Citation

@article{chen2022simple,
  title={Simple Baselines for Image Restoration},
  author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},
  journal={arXiv preprint arXiv:2204.04676},
  year={2022}
}

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