{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rmuF9iKWTbdk"
      },
      "outputs": [],
      "source": [
        "! pip install -q super_gradients==3.7.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dTB0jy_NNSFz"
      },
      "outputs": [],
      "source": [
        "from super_gradients.common.object_names import Models\n",
        "from super_gradients.conversion import DetectionOutputFormatMode\n",
        "from super_gradients.training import models\n",
        "\n",
        "model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GymUghyCNXem"
      },
      "outputs": [],
      "source": [
        "# export the model for compatibility with Frigate\n",
        "\n",
        "model.export(\"yolo_nas_s.onnx\",\n",
        "             output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
        "             max_predictions_per_image=20,\n",
        "             confidence_threshold=0.4,\n",
        "             input_image_shape=(320,320),\n",
        "            )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "uBhXV5g4Nh42",
        "outputId": "303104fa-97dd-4efd-8b56-808e0ea00166"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_9976a3d8-3025-4b5b-b060-6a3aed3c752f\", \"yolo_nas_s.onnx\", 48803558)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('yolo_nas_s.onnx')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}