--- title: "Export anomaly model" slug: "export-download-model" updated: 2025-05-02T10:01:06Z published: 2025-05-02T10:01:06Z canonical: "docs.akridata.ai/export-download-model" --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.akridata.ai/llms.txt > Use this file to discover all available pages before exploring further. # Export anomaly model Once the anomaly segmentation session run is complete, you can export the docker model and download it locally or on a different machine. ## Export Model 1. In the model screen, click the **Export Model** button. If you want to apply a custom rule in the exported model, then load that rule by clicking the **Saved Rules** button. ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/anomaly-export-model(1).jpg) Once the model gets exported, the button label is displayed as Download Model. 2. Click the **Download Model** button. ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/anomaly-download-model(1).jpg) You can click the **Download Model** button to download the docker image. Follow the necessary steps as mentioned in the next section to extract the docker image and run the docker. ## Download Model 1. Click **Download****Model** to view the available download options as shown below. ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/anomaly-seg-download-export(2).jpg) 1. **Copy URL**: Click the copy icon to copy the URL of the exported model to download it on a different machine. 2. **Copy Path**: Click the copy icon to copy the image path and follow the instruction by using the required credentials. 3. **Download**: Click this option to download the exported model to the same system. > [!NOTE] > X86-64 support > > The downloaded docker image can run only on X86-64 hardware and specifically does not support the ARM based Mac M1 and similar architectures. The next sections explain how to use the downloaded docker for inference. ### Download Docker image using Copy URL 1. Click the copy icon to copy the URL of the export model and download it on a different machine. ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/docker-export-tar.jpg) 2. Load the exported model as docker image, using the following command: ```shell docker load -i ``` ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/docker-load-image.jpg) The command prompt displays the docker image name under REPOSITORY. ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/docker-load-image-repository.jpg) 3. Run the docker container, using the following command: ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/docker-run-command.jpg) 4. Verify if the container is running, using the following command: ```shell docker ps command. ``` ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/docker-image-verify.jpg) ## Run Inference The downloaded docker image adheres to [kserve inference server interface](https://kserve.github.io/website/latest/reference/api/). The following steps describe running inference through kserve HTTP interface. The name of the kserve model is **‘anoseg_ensemble’.** 1. Check if the model is available by using the following command: ```shell curl http://localhost:8000/v2/models/anoseg_ensemble ``` This should display the metadata for the model, as follows. ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/docker-image-updated(1).jpg) 2. Create input.json for the inference. 1. id: Any string to identify this specific inference. 2. parameters 1. frame_height and frame_width: Image height and width in pixels. 2. frame_type: Input image file format (e.g., jpeg, png) 3. inputs 1. name: Must be “INPUT_IMG” 2. shape: 1-D array with size of the image file. In the below example, the image file size is 35581 bytes. 3. datatype: Must be UINT8 4. data: The bytes array filled by reading the image file. ```shell {  "id": "inference_request_001",  "parameters": {    "frame_height": 512,    "frame_width": 512,    "frame_type": "jpeg"  },  "inputs": [    {      "name": "INPUT_IMG",      "shape": [35581],      "datatype": "UINT8",      "parameters": {},      "data": [255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 1, 0, 96, 0, 96, 0, 0, ...]    }  ] } ``` 3. Run inference by invoking the REST endpoint. A curl command example is provided below. ```shell curl -X POST http://localhost:8000/v2/models/anoseg_ensemble/infer \     -H "Content-Type: application/json" \     -d @${HOME}/Downloads/input.json > result.json ``` The kserve inference displays the following output as a response: ```shell "outputs": [  {    "name": "OUTPUT_LABELS",    "datatype": "FP32",    "shape": [-1]  },  {    "name": "OUTPUT_SCORES",    "datatype": "FP32",    "shape": [-1]  },  {    "name": "OUTPUT_AMAP_IMAGE",    "datatype": "UINT8",    "shape": [-1]  } ] ``` > [!NOTE] > “shape”: [-1] indicates variable-length output, depending on the image size or compression. The output field descriptions are as follows: | Output Name | Data Type | Description | | --- | --- | --- | | OUTPUT_LABELS | FP32 | Predicted label. 0 = Normal, 1 = Anomaly | | OUTPUT_SCORES | FP32 | Confidence score for the predicted label. Range 0.0 to 1.0 | | OUTPUT_AMAP_IMAGE | UINT8 | Flattened JPEG image (as byte array) showing the visual anomaly heatmap. | An example inference response would be as follows: ```shell {  "model_name": "anoseg_ensemble",  "outputs": [    {      "name": "OUTPUT_LABELS",      "datatype": "FP32",      "shape": [1],      "data": [1.0]    },    {      "name": "OUTPUT_SCORES",      "datatype": "FP32",      "shape": [1],      "data": [0.9123]    },    {      "name": "OUTPUT_AMAP_IMAGE",      "datatype": "UINT8",      "shape": [13248],      "data": [255, 216, 255, 224, ...]  // JPEG byte array    }  ] } ``` 4. A sample program to visualize the anomaly maps for labels, score and heatmap, is shown below. ```shell import json import numpy as np import cv2 with open("result.json", "r") as f: # FIle path for result.json    result = json.load(f) output = result["outputs"] amap_output = next(x for x in output if x["name"] == "OUTPUT_AMAP_IMAGE") amap_bytes = np.array(amap_output["data"], dtype=np.uint8) OUTPUT_SCORES = next(x for x in output if x["name"] == "OUTPUT_SCORES")["data"] OUTPUT_LABELS = next(x for x in output if x["name"] == "OUTPUT_LABELS")["data"] decoded_img = cv2.imdecode(amap_bytes, cv2.IMREAD_COLOR) print(f"OUTPUT_LABELS : {OUTPUT_LABELS}") print(f"OUTPUT_SCORES : {OUTPUT_SCORES}") if decoded_img is None:    print("Failed to decode image") else:    print("Image decoded")    cv2.imshow("Anomaly Heatmap", decoded_img)    cv2.waitKey(0)    cv2.destroyAllWindows() ``` ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/heatmap.jpg) The labels and scores will appear as follows: ![](https://cdn.document360.io/3e9d4528-fbc6-4948-a804-8ee7068e7ac3/Images/Documentation/image-decoed.jpg) ##