yosedie/carpet-analysis-api

🧶 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.

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🚀 Live Demo & API Endpoint

These models power the active production backend deployed on Hugging Face Spaces:

• Live Space: yosedie/carpet-analysis-api
• Interactive Swagger UI: https://yosedie-carpet-analysis-api.hf.space/docs

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📦 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

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⚡ Quickstart: Python & ONNX Runtime

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)

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👤 Author

• Developer: yosedie
• GitHub: https://github.com/yosedie