SebRincon
/

nafnet-realestate

@@ -1,139 +1,166 @@
-# NAFNet Real Estate Enhancement - Benchmark Results
-## Model Information
 | Metric | Value |
 |--------|-------|
 | **Architecture** | NAFNet (width=32) |
 | **Parameters** | 29.2 million |
-| **Model Size** | 111 MB (FP32) |
 | **Training Time** | 5 hours |
 | **Training Images** | 577 pairs |
 | **Final PSNR** | 21.69 dB |
 | **Final SSIM** | 0.8968 |
-## Benchmark Configuration
-- **Test Images**: 100 high-resolution real estate photos
-- **Average Resolution**: 3300x2200 (7.25 megapixels)
-- **Hardware**: NVIDIA GB10 (128GB unified memory)
-- **Framework**: PyTorch 2.9 + BasicSR
-## Performance Results
-### Timing
 | Metric | Value |
 |--------|-------|
-| **Total time (100 images)** | 6.7 minutes |
-| **Average per image** | 4.0 seconds |
-| **Minimum** | 3.25 seconds |
-| **Maximum** | 4.53 seconds |
-| **Throughput** | 0.25 images/second |
-| **Megapixels/second** | 1.81 MP/s |
-### Memory Usage - RAM
-| Stage | RAM Usage |
-|-------|-----------|
-| **Baseline (Python)** | 736 MB |
-| **After model load** | 905 MB |
-| **Peak during inference** | 1,317 MB |
-| **Model overhead** | +169 MB |
-| **Inference overhead** | +412 MB |
-| **Total net usage** | **581 MB** |
-### Memory Usage - GPU
-| Metric | Value |
-|--------|-------|
-| **Model size** | 111 MB |
-| **Peak allocated** | 8,258 MB |
-| **Peak reserved** | 12,050 MB |
-| **Net for inference** | 8,147 MB |
-## Scaling by Resolution
-| Resolution | Megapixels | Est. RAM | Est. GPU | Est. 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 |
 ## Mobile Deployment (iOS)
-| Format | Size | Precision |
-|--------|------|-----------|
-| PyTorch | 111 MB | FP32 |
-| ONNX | 112 MB | FP32 |
-| Core ML | ~56 MB | FP16 |
-### iOS Memory Requirements
-All resolutions fit within 3-4 GB RAM budget:
-- 1080p: 150-250 MB ✅
-- 1440p: 250-400 MB ✅
-- 3K: 500-800 MB ✅
-- 4K: 600-900 MB ✅
-## Raw Benchmark Output
-```
-============================================================
-NAFNet INFERENCE BENCHMARK
-============================================================
-[BASELINE MEMORY]
-  Process RAM: 735.6 MB
-  GPU Memory:  0.0 MB
-[LOADING MODEL]
-  Path: BasicSR/experiments/NAFNet_RealEstate_Fast/models/net_g_12000.pth
-  Load time:   0.47s
-  Model RAM:   169.1 MB (process now: 904.7 MB)
-  Model GPU:   111.3 MB (allocated now: 111.3 MB)
-[PROCESSING 100 IMAGES]
-------------------------------------------------------------
-  [  1/100] 0000_val.jpg: 3301x2199 (7.26MP) | 4.525s | RAM: 1254MB | GPU: 111MB
-  [  2/100] 0001_val.jpg: 3299x2200 (7.26MP) | 3.975s | RAM: 1254MB | GPU: 111MB
-  ...
-  [100/100] 0519_train.jpg: 3300x2199 (7.26MP) | 4.114s | RAM: 1295MB | GPU: 111MB
-============================================================
-BENCHMARK RESULTS
-============================================================
-[IMAGES PROCESSED]
-  Total:       100 images
-  Megapixels:  724.6 MP total (7.25 MP avg)
-[TIMING]
-  Total time:  399.88s
-  Avg/image:   3.999s
-  Min:         3.252s
-  Max:         4.525s
-  Throughput:  0.25 img/s
-  MP/second:   1.81 MP/s
-[MEMORY - RAM]
-  Baseline:    735.6 MB
-  After model: 904.7 MB (+169.1 MB for model)
-  Peak:        1316.6 MB
-  Net usage:   581.0 MB (model + inference)
-[MEMORY - GPU]
-  Model size:      111.3 MB
-  Peak allocated:  8258.4 MB
-  Peak reserved:   12050.0 MB
-  Net for inference: 8147.1 MB
-============================================================
 ```
-## Files
-Models available on Hugging Face: `SebRincon/pixel-sorcery`
-- `nafnet_realestate.pth` - Trained PyTorch model
-- `nafnet_realestate.onnx` - ONNX format for deployment

+---
+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
+```python
+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
+```python
+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):
+1. Convert ONNX to Core ML on macOS:
+```bash
+pip install coremltools
+python convert_on_mac.py
+```
+2. Add `.mlpackage` to Xcode project
+3. 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
+```bibtex
+@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}
+}
 ```
+## Links
+- **GitHub**: [SebRincon/pixel-sorcery](https://github.com/SebRincon/pixel-sorcery/tree/sebastian/nafnet-realestate)
+- **Original NAFNet**: [megvii-research/NAFNet](https://github.com/megvii-research/NAFNet)