---
license: mit
library_name: onnx
pipeline_tag: image-segmentation
tags:
  - computer-vision
  - onnx
  - carpet-analysis
  - defect-detection
  - unet
  - sam2
  - image-classification
---

# 🧶 Carpet Analysis System - Deep Learning Models

A collection of pre-trained deep learning models optimized for real-time carpet quality inspection, style classification, material detection, and surface segmentation.

Optimized with **ONNX Runtime** for low latency and efficient deployment on both CPU and GPU backends.

---

## 🚀 Live Demo & API Endpoint

These models power the active production backend deployed on Hugging Face Spaces:
* **Live Space:** [yosedie/carpet-analysis-api](https://huggingface.co/spaces/yosedie/carpet-analysis-api)
* **Interactive Swagger UI:** [https://yosedie-carpet-analysis-api.hf.space/docs](https://yosedie-carpet-analysis-api.hf.space/docs)

---

## 📦 Model Portfolio

| Model File | Architecture | Task / Objective | Input Resolution | Format |
| :--- | :--- | :--- | :--- | :--- |
| **`unet_model.onnx`** | U-Net | Carpet surface segmentation & defect coverage ratio calculation | 512x512 | ONNX |
| **`cnn_a_model.onnx`** | Custom CNN | Carpet Style Classification (Asia, Malaysia, Mie, Persia, Polos, Sejadah) | 224x224 | ONNX |
| **`cnn_b_model.onnx`** | Custom CNN | Fiber Material Identification (Polypropylene, Nylon, Polyester) | 224x224 | ONNX |
| **`sam2_hiera_base_plus.pt`** | SAM 2 (Hiera Base+) | Zero-shot promptable segmentation for fine-grained boundary extraction | Multi-scale | PyTorch / SAM 2 |

---

## ⚡ Quickstart: Python & ONNX Runtime

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

# 1. Initialize U-Net Inference Session
session = ort.InferenceSession("unet_model.onnx", providers=["CPUExecutionProvider"])

# 2. Preprocess Input Image
image = Image.open("sample_carpet.jpg").convert("RGB").resize((512, 512))
img_data = np.array(image, dtype=np.float32) / 255.0
img_data = np.transpose(img_data, (2, 0, 1))
img_data = np.expand_dims(img_data, axis=0)

# 3. Execute Model Prediction
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
prediction = session.run([output_name], {input_name: img_data})[0]

# 4. Generate Binary Mask
segmentation_mask = (prediction[0, 0] > 0.5).astype(np.uint8) * 255
print("Inference successful. Mask dimension:", segmentation_mask.shape)
```

---

## 👤 Author
* **Developer:** [yosedie](https://huggingface.co/yosedie)
* **GitHub:** [https://github.com/yosedie](https://github.com/yosedie)