# U-Net Image Segmentation Inference with AMD MIGraphX
This examples provides a simple example for utilizing U-Net ONNX model for image segmentation, using AMD MIGraphX graph optimization engine for fast inference.
## How-to
Please utilize the notebook given `unet_inference.ipynb`.
## Model Details
ONNX model utilized in this example can be found [here](https://www.dropbox.com/s/3ntkhyk30x05uuv/unet_13_256.onnx).
"mask = model.run({'inputs':input_im}) # Your first inference would take longer than the following ones.\n",
"output_mask = np.array(mask[0])\n",
"print(output_mask.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acbd68e3",
"metadata": {},
"outputs": [],
"source": [
"def sigmoid(x):\n",
" return 1 / (1 + np.exp(-x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58e3062c",
"metadata": {},
"outputs": [],
"source": [
"probs = sigmoid(output_mask)\n",
"full_mask = probs > 0.996\n",
"plot_img_and_mask(imPrint, full_mask)"
]
},
{
"cell_type": "markdown",
"id": "6126df0b",
"metadata": {},
"source": [
"<b>NOTE:</b> The model weights utilized here are trained by using car images with plain backgrounds. The imperfect result on a \"real-world\" image as shown above is expected. To get a better result fine-tuning the model on a dataset of real-world examples is recommended. "
