how-to-use-scivision.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to use `scivision`\n",
    "\n",
    "In this notebook, we will:\n",
    "\n",
    "1. Demonstrate using the scivision [Python API](https://scivision.readthedocs.io/en/latest/api.html) to load several pretrained image classification models\n",
    "2. Use the scivision catalog to find a matching dataset, which the model can be run on\n",
    "3. Run the model on the data, performing simple model inference\n",
    "4. Use the scivision catalog to find another model that can be run on the same dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's import some things from scivision: `default_catalog` is a scivision **catalog** that will let us discover models and datasets, and `load_pretrained_model` provides a convenient way to load and run a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scivision import default_catalog, load_pretrained_model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting our model in the scivision catalog\n",
    "\n",
    "A scivision catalog is a collection of **models** and **datasources**.\n",
    "\n",
    "For this example, we want to find datasources compatible with the model catalog entry \"image-classifiers\".  But first, let's use the catalog to retrive the \"image-classifiers\" repository url and take a look at the data contained in the *default catalog* (the built-in catalog, distributed as part of scivision) and see how this is structured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FlexibleUrl('https://github.com/alan-turing-institute/scivision_classifier', scheme='https', host='github.com', tld='com', host_type='domain', path='/alan-turing-institute/scivision_classifier')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the model repo url\n",
    "models_catalog = default_catalog.models.to_dataframe()\n",
    "model_repo = models_catalog[models_catalog.name == \"image-classifiers\"].url.item()\n",
    "model_repo # Why not paste the repo link into your browser and see how it looks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>tasks</th>\n",
       "      <th>url</th>\n",
       "      <th>pkg_url</th>\n",
       "      <th>format</th>\n",
       "      <th>scivision_usable</th>\n",
       "      <th>pretrained</th>\n",
       "      <th>labels_required</th>\n",
       "      <th>institution</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>image-classifiers</td>\n",
       "      <td>Scivision plugin for the image-classifiers pac...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(classification, 2D, image)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name                                        description  \\\n",
       "6  image-classifiers  Scivision plugin for the image-classifiers pac...   \n",
       "\n",
       "                         tasks  \\\n",
       "6  (TaskEnum.classificiation,)   \n",
       "\n",
       "                                                 url  \\\n",
       "6  https://github.com/alan-turing-institute/scivi...   \n",
       "\n",
       "                                             pkg_url format  scivision_usable  \\\n",
       "6  git+https://github.com/alan-turing-institute/s...  image              True   \n",
       "\n",
       "   pretrained  labels_required               institution  \\\n",
       "6        True            False  (alan-turing-institute,)   \n",
       "\n",
       "                          tags  \n",
       "6  (classification, 2D, image)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspecting model entry and its metadata in the default catalog\n",
    "models_catalog[models_catalog.name == \"image-classifiers\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the model\n",
    "\n",
    "Some model entries in the scivision catalog contain a single loadable model. Here, let's load the \"image-classifiers\" entry, which contains each of the models in the [image-classifiers](https://pypi.org/project/image-classifiers/) package. We can do this with the `load_pretrained_model` function. We'll load 2 models (`resnet18` and `densenet121`).\n",
    "\n",
    "Note: the \"model_selection\" arg is not mandatory and will have a default value for any model catalog entries that have more than one model - for model catalog entries with a single model, this need not be set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-27 15:52:23.925836: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Note: when loading this model repo for the first time you'll need to add the allow_install=True flag\n",
    "resnet_model = load_pretrained_model(model_repo, model_selection='resnet18', allow_install=True)\n",
    "densenet_model = load_pretrained_model(model_repo, model_selection='densenet121')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scivision.PretrainedModel( \n",
       "  module='scivision_classifier', \n",
       "  model='resnet18', \n",
       "  source='https://github.com/alan-turing-institute/scivision_classifier' \n",
       "  pipe='DataPipe(input=<Parameter \"X: numpy.ndarray\">, output=<Parameter \"image: numpy.ndarray\">)' \n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's explore one of the model objects\n",
    "resnet_model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later, we'll use these models to make predictions on image data found in the scivision catalog."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query the default scivision data catalog"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use the `default_catalog` to identify datasources in the catalog that are compatible with our models (based on `tasks`, `format` and `labels_provided`/`labels_required`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>tasks</th>\n",
       "      <th>labels_provided</th>\n",
       "      <th>domains</th>\n",
       "      <th>url</th>\n",
       "      <th>format</th>\n",
       "      <th>institution</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>scivision-test-data</td>\n",
       "      <td>Test data comprised of individual images inclu...</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
       "      <td>True</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>image</td>\n",
       "      <td>(Wikipedia by Erik Veland,)</td>\n",
       "      <td>(demo,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cefas-plankton</td>\n",
       "      <td>Sample dataset of Plankton required to demonst...</td>\n",
       "      <td>(TaskEnum.classificiation)</td>\n",
       "      <td>True</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://raw.githubusercontent.com/alan-turing-...</td>\n",
       "      <td>image</td>\n",
       "      <td>(Centre for Environment, Fisheries and Aquacul...</td>\n",
       "      <td>(2D, plankton, ecology, environmental-science)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>coastal-edges</td>\n",
       "      <td>Sample dataset of satellite RGB imagery over t...</td>\n",
       "      <td>(TaskEnum.segmentation, TaskEnum.classificiation)</td>\n",
       "      <td>False</td>\n",
       "      <td>(computer-vision, earth-observation, ecology)</td>\n",
       "      <td>https://github.com/MartinSJRogers/VEdge_Detect...</td>\n",
       "      <td>image</td>\n",
       "      <td>(Cambridge University,)</td>\n",
       "      <td>(2D, satellite, remote-sensing, ecology, envir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sentinel2_stac</td>\n",
       "      <td>Data plugin for accessing Sentinel-2 Cloud-Opt...</td>\n",
       "      <td>(TaskEnum.segmentation, TaskEnum.classificiation)</td>\n",
       "      <td>False</td>\n",
       "      <td>(computer-vision, earth-observation, ecology)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>image</td>\n",
       "      <td>(The Alan Turing Institute,)</td>\n",
       "      <td>(satellite, aerial, remote-sensing, ecology, e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>flowers</td>\n",
       "      <td>The 102 flowers dataset, created by Visual Geo...</td>\n",
       "      <td>(TaskEnum.classificiation)</td>\n",
       "      <td>True</td>\n",
       "      <td>(computer-vision, ecology)</td>\n",
       "      <td>https://github.com/nbarlowATI/flowers_data_repo</td>\n",
       "      <td>image</td>\n",
       "      <td>(The Alan Turing Institute,)</td>\n",
       "      <td>(ecology, environmental-science)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>butterflies</td>\n",
       "      <td>The Butterfly Image Classification Kaggle chal...</td>\n",
       "      <td>(TaskEnum.classificiation)</td>\n",
       "      <td>True</td>\n",
       "      <td>(computer-vision, ecology)</td>\n",
       "      <td>https://github.com/nbarlowATI/butterfly_data_repo</td>\n",
       "      <td>image</td>\n",
       "      <td>(The Alan Turing Institute,)</td>\n",
       "      <td>(butterflies, insects, animals, ecology)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Parakeet</td>\n",
       "      <td>Synthetic cryoEM data from Parakeet</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>https://github.com/rosalindfranklininstitute/s...</td>\n",
       "      <td>image</td>\n",
       "      <td>(Rosalind Franklin Institute,)</td>\n",
       "      <td>(CryoEM,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cell-cycle-scivision</td>\n",
       "      <td>Crops of H2B-FP stained nuclei in MDCK cells.</td>\n",
       "      <td>(TaskEnum.classificiation)</td>\n",
       "      <td>True</td>\n",
       "      <td>(optical microscopy, cell biology)</td>\n",
       "      <td>https://github.com/lowe-lab-ucl/cell-cycle-sci...</td>\n",
       "      <td>image</td>\n",
       "      <td>(UCL,)</td>\n",
       "      <td>()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MapReader_Data_SIGSPATIAL_2022</td>\n",
       "      <td>Here we share gold standard annotations and ou...</td>\n",
       "      <td>(TaskEnum.classificiation)</td>\n",
       "      <td>True</td>\n",
       "      <td>(history, urban-planning)</td>\n",
       "      <td>https://github.com/alan-turing-institute/mapre...</td>\n",
       "      <td>image</td>\n",
       "      <td>(The Alan Turing Institute,)</td>\n",
       "      <td>(2D, humanities)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name  \\\n",
       "2              scivision-test-data   \n",
       "3                   cefas-plankton   \n",
       "4                    coastal-edges   \n",
       "6                   sentinel2_stac   \n",
       "7                          flowers   \n",
       "8                      butterflies   \n",
       "9                         Parakeet   \n",
       "10            cell-cycle-scivision   \n",
       "12  MapReader_Data_SIGSPATIAL_2022   \n",
       "\n",
       "                                          description  \\\n",
       "2   Test data comprised of individual images inclu...   \n",
       "3   Sample dataset of Plankton required to demonst...   \n",
       "4   Sample dataset of satellite RGB imagery over t...   \n",
       "6   Data plugin for accessing Sentinel-2 Cloud-Opt...   \n",
       "7   The 102 flowers dataset, created by Visual Geo...   \n",
       "8   The Butterfly Image Classification Kaggle chal...   \n",
       "9                 Synthetic cryoEM data from Parakeet   \n",
       "10     Crops of H2B-FP stained nuclei in MDCK cells.    \n",
       "12  Here we share gold standard annotations and ou...   \n",
       "\n",
       "                                                tasks  labels_provided  \\\n",
       "2   (TaskEnum.object_detection, TaskEnum.segmentat...             True   \n",
       "3                          (TaskEnum.classificiation)             True   \n",
       "4   (TaskEnum.segmentation, TaskEnum.classificiation)            False   \n",
       "6   (TaskEnum.segmentation, TaskEnum.classificiation)            False   \n",
       "7                          (TaskEnum.classificiation)             True   \n",
       "8                          (TaskEnum.classificiation)             True   \n",
       "9   (TaskEnum.object_detection, TaskEnum.segmentat...            False   \n",
       "10                         (TaskEnum.classificiation)             True   \n",
       "12                         (TaskEnum.classificiation)             True   \n",
       "\n",
       "                                          domains  \\\n",
       "2                              (computer-vision,)   \n",
       "3                              (computer-vision,)   \n",
       "4   (computer-vision, earth-observation, ecology)   \n",
       "6   (computer-vision, earth-observation, ecology)   \n",
       "7                      (computer-vision, ecology)   \n",
       "8                      (computer-vision, ecology)   \n",
       "9                                            None   \n",
       "10             (optical microscopy, cell biology)   \n",
       "12                      (history, urban-planning)   \n",
       "\n",
       "                                                  url format  \\\n",
       "2   https://github.com/alan-turing-institute/scivi...  image   \n",
       "3   https://raw.githubusercontent.com/alan-turing-...  image   \n",
       "4   https://github.com/MartinSJRogers/VEdge_Detect...  image   \n",
       "6   https://github.com/alan-turing-institute/scivi...  image   \n",
       "7     https://github.com/nbarlowATI/flowers_data_repo  image   \n",
       "8   https://github.com/nbarlowATI/butterfly_data_repo  image   \n",
       "9   https://github.com/rosalindfranklininstitute/s...  image   \n",
       "10  https://github.com/lowe-lab-ucl/cell-cycle-sci...  image   \n",
       "12  https://github.com/alan-turing-institute/mapre...  image   \n",
       "\n",
       "                                          institution  \\\n",
       "2                         (Wikipedia by Erik Veland,)   \n",
       "3   (Centre for Environment, Fisheries and Aquacul...   \n",
       "4                             (Cambridge University,)   \n",
       "6                        (The Alan Turing Institute,)   \n",
       "7                        (The Alan Turing Institute,)   \n",
       "8                        (The Alan Turing Institute,)   \n",
       "9                      (Rosalind Franklin Institute,)   \n",
       "10                                             (UCL,)   \n",
       "12                       (The Alan Turing Institute,)   \n",
       "\n",
       "                                                 tags  \n",
       "2                                             (demo,)  \n",
       "3      (2D, plankton, ecology, environmental-science)  \n",
       "4   (2D, satellite, remote-sensing, ecology, envir...  \n",
       "6   (satellite, aerial, remote-sensing, ecology, e...  \n",
       "7                    (ecology, environmental-science)  \n",
       "8            (butterflies, insects, animals, ecology)  \n",
       "9                                           (CryoEM,)  \n",
       "10                                                 ()  \n",
       "12                                   (2D, humanities)  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compatible_datasources = default_catalog.compatible_datasources(\"image-classifiers\").to_dataframe()\n",
    "compatible_datasources"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `scivision-test-data`, an image dataset containing a single image of a Koala."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>tasks</th>\n",
       "      <th>labels_provided</th>\n",
       "      <th>domains</th>\n",
       "      <th>url</th>\n",
       "      <th>format</th>\n",
       "      <th>institution</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>scivision-test-data</td>\n",
       "      <td>Test data comprised of individual images inclu...</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
       "      <td>True</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>image</td>\n",
       "      <td>(Wikipedia by Erik Veland,)</td>\n",
       "      <td>(demo,)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name                                        description  \\\n",
       "2  scivision-test-data  Test data comprised of individual images inclu...   \n",
       "\n",
       "                                               tasks  labels_provided  \\\n",
       "2  (TaskEnum.object_detection, TaskEnum.segmentat...             True   \n",
       "\n",
       "              domains                                                url  \\\n",
       "2  (computer-vision,)  https://github.com/alan-turing-institute/scivi...   \n",
       "\n",
       "  format                  institution     tags  \n",
       "2  image  (Wikipedia by Erik Veland,)  (demo,)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_datasource = compatible_datasources.loc[compatible_datasources['name'] == 'scivision-test-data']\n",
    "target_datasource"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset\n",
    "\n",
    "Now let's load the dataset using the scivision python API, specifically the [load_dataset](https://scivision.readthedocs.io/en/latest/api.html#scivision.io.reader.load_dataset) function, which takes as input the url of the data repository (structured as per [this template](https://scivision.readthedocs.io/en/latest/data_repository_template.html)), which we can get from the target datasource:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scivision import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = target_datasource['url'].item()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The returned data config object of the`load_dataset` function is an \"intake catalog\". You can read our [documentation](https://scivision.readthedocs.io/en/latest/data_repository_template.html#data-config-file) to understand this better, but for now, let's inspect this config:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/yaml": "data:\n  args:\n    path: https://raw.githubusercontent.com/alan-turing-institute/scivision-test-data/main/.scivision/data.yml\n  description: ''\n  driver: intake.catalog.local.YAMLFileCatalog\n  metadata: {}\n",
      "text/plain": [
       "data:\n",
       "  args:\n",
       "    path: https://raw.githubusercontent.com/alan-turing-institute/scivision-test-data/main/.scivision/data.yml\n",
       "  description: ''\n",
       "  driver: intake.catalog.local.YAMLFileCatalog\n",
       "  metadata: {}\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "data"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_config = load_dataset(data_url)\n",
    "data_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test_image', 'airport', 'dog_cat']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data_config)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clicking the `path` link to the location of this data config file online (in the dataset repo) reveals that there is one data source called `test_image`, and that the `intake_xarray.image.ImageSource` \"intake driver\" is being used. We can retrive the test image data in an image format which the model will accept, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/yaml": "test_image:\n  args:\n    urlpath: https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Cutest_Koala.jpg/262px-Cutest_Koala.jpg\n  description: Load a single Koala\n  driver: intake_xarray.image.ImageSource\n  metadata:\n    catalog_dir: https://raw.githubusercontent.com/alan-turing-institute/scivision-test-data/main/.scivision\n",
      "text/plain": [
       "test_image:\n",
       "  args:\n",
       "    urlpath: https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Cutest_Koala.jpg/262px-Cutest_Koala.jpg\n",
       "  description: Load a single Koala\n",
       "  driver: intake_xarray.image.ImageSource\n",
       "  metadata:\n",
       "    catalog_dir: https://raw.githubusercontent.com/alan-turing-institute/scivision-test-data/main/.scivision\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "test_image"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_config['test_image']()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ifenton/opt/anaconda3/envs/scivision_comp/lib/python3.9/site-packages/xarray/core/dataarray.py:1323: FutureWarning: None value for 'chunks' is deprecated. It will raise an error in the future. Use instead '{}'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 240, x: 262, channel: 3)&gt;\n",
       "dask.array&lt;xarray-&lt;this-array&gt;, shape=(240, 262, 3), dtype=uint8, chunksize=(240, 262, 3), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * y        (y) int64 0 1 2 3 4 5 6 7 8 ... 231 232 233 234 235 236 237 238 239\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 8 ... 253 254 255 256 257 258 259 260 261\n",
       "  * channel  (channel) int64 0 1 2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 240</li><li><span class='xr-has-index'>x</span>: 262</li><li><span class='xr-has-index'>channel</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-14c0a2d0-d2ef-4242-88d2-decdf477efd5' class='xr-array-in' type='checkbox' checked><label for='section-14c0a2d0-d2ef-4242-88d2-decdf477efd5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(240, 262, 3), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 184.22 kiB </td>\n",
       "                        <td> 184.22 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (240, 262, 3) </td>\n",
       "                        <td> (240, 262, 3) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"150\" height=\"234\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"74\" y2=\"64\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"120\" x2=\"74\" y2=\"184\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"74\" y1=\"64\" x2=\"74\" y2=\"184\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 74.66097889537495,64.66097889537495 74.66097889537495,184.66097889537497 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"74\" y1=\"64\" x2=\"100\" y2=\"64\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"74\" y2=\"64\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"36\" y1=\"0\" x2=\"100\" y2=\"64\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 36.07278677020334,0.0 100.73376566557829,64.66097889537495 74.66097889537495,64.66097889537495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"74\" y1=\"64\" x2=\"100\" y2=\"64\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"74\" y1=\"184\" x2=\"100\" y2=\"184\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"74\" y1=\"64\" x2=\"74\" y2=\"184\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"100\" y1=\"64\" x2=\"100\" y2=\"184\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"74.66097889537495,64.66097889537495 100.73376566557829,64.66097889537495 100.73376566557829,184.66097889537497 74.66097889537495,184.66097889537497\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"87.697372\" y=\"204.660979\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
       "  <text x=\"120.733766\" y=\"124.660979\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.733766,124.660979)\">262</text>\n",
       "  <text x=\"32.330489\" y=\"172.330489\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,32.330489,172.330489)\">240</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-8f5088fa-c53e-4398-99b8-bfb391666d15' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f5088fa-c53e-4398-99b8-bfb391666d15' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 235 236 237 238 239</div><input id='attrs-1e866439-080a-46b7-9425-ebd836715437' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e866439-080a-46b7-9425-ebd836715437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fe7e91d-eb07-464f-b030-9d4996aab515' class='xr-var-data-in' type='checkbox'><label for='data-5fe7e91d-eb07-464f-b030-9d4996aab515' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 237, 238, 239])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 257 258 259 260 261</div><input id='attrs-31435ca8-9820-44ed-9332-a81f2d0e97ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31435ca8-9820-44ed-9332-a81f2d0e97ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66066ad9-4c8b-4404-8a77-6e4b252f474d' class='xr-var-data-in' type='checkbox'><label for='data-66066ad9-4c8b-4404-8a77-6e4b252f474d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 259, 260, 261])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-ef910ab5-f543-4a51-83b0-fcf958477a5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef910ab5-f543-4a51-83b0-fcf958477a5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ed6f92f-0cbf-40c2-b1d9-8be03ed2e5c5' class='xr-var-data-in' type='checkbox'><label for='data-7ed6f92f-0cbf-40c2-b1d9-8be03ed2e5c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-101fc54a-2495-469c-b071-562e0f1046cb' class='xr-section-summary-in' type='checkbox'  ><label for='section-101fc54a-2495-469c-b071-562e0f1046cb' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a222840e-6825-4b5c-9994-e21299904a3c' class='xr-index-data-in' type='checkbox'/><label for='index-a222840e-6825-4b5c-9994-e21299904a3c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            230, 231, 232, 233, 234, 235, 236, 237, 238, 239],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;, length=240))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d901eaef-d499-4ee2-830b-26b3211ecb52' class='xr-index-data-in' type='checkbox'/><label for='index-d901eaef-d499-4ee2-830b-26b3211ecb52' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            252, 253, 254, 255, 256, 257, 258, 259, 260, 261],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;, length=262))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>channel</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2275cdb8-308a-4c0e-a07b-e653bd720eff' class='xr-index-data-in' type='checkbox'/><label for='index-2275cdb8-308a-4c0e-a07b-e653bd720eff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2], dtype=&#x27;int64&#x27;, name=&#x27;channel&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90b3db0c-e02d-45a5-9c33-71d95383bfdc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-90b3db0c-e02d-45a5-9c33-71d95383bfdc' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 240, x: 262, channel: 3)>\n",
       "dask.array<xarray-<this-array>, shape=(240, 262, 3), dtype=uint8, chunksize=(240, 262, 3), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * y        (y) int64 0 1 2 3 4 5 6 7 8 ... 231 232 233 234 235 236 237 238 239\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 8 ... 253 254 255 256 257 258 259 260 261\n",
       "  * channel  (channel) int64 0 1 2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image = data_config.test_image().to_dask() # The xarray.DataArray is one format accepted by the our models\n",
    "test_image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the image with `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d9119940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_image)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model predictions\n",
    "\n",
    "Now let's use the loaded model on the test image data we found in the via catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 282ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'koala : 99.80%'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet_model.predict(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'koala : 99.73%'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "densenet_model.predict(test_image)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the models have given predictions for the test image, with a confidence score. Check out the code in the [model repo](https://github.com/alan-turing-institute/scivision_classifier) to see how this was determined!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query the default scivision model catalog\n",
    "\n",
    "Using our test Koala image dataset, let's search the scivision default catalog for other models that can be used with it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>tasks</th>\n",
       "      <th>url</th>\n",
       "      <th>pkg_url</th>\n",
       "      <th>format</th>\n",
       "      <th>scivision_usable</th>\n",
       "      <th>pretrained</th>\n",
       "      <th>labels_required</th>\n",
       "      <th>institution</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>stardist</td>\n",
       "      <td>Single class object detection and segementatio...</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
       "      <td>https://github.com/stardist/stardist</td>\n",
       "      <td>git+https://github.com/stardist/stardist.git@main</td>\n",
       "      <td>image</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(epfl,)</td>\n",
       "      <td>(2D, 3D, optical-microscopy, xray, microtomogr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PlantCV</td>\n",
       "      <td>Open-source image analysis software package ta...</td>\n",
       "      <td>(TaskEnum.segmentation, TaskEnum.thresholding,...</td>\n",
       "      <td>https://github.com/danforthcenter/plantcv</td>\n",
       "      <td>git+https://github.com/danforthcenter/plantcv@...</td>\n",
       "      <td>image</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(danforthcenter,)</td>\n",
       "      <td>(2D, hyperspectral, multispectral, near-infrar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>greenotyper</td>\n",
       "      <td>Greenotyper is an image analysis tool for larg...</td>\n",
       "      <td>(TaskEnum.segmentation,)</td>\n",
       "      <td>https://github.com/MarniTausen/Greenotyper</td>\n",
       "      <td>git+https://github.com/MarniTausen/Greenotyper...</td>\n",
       "      <td>image</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(MarniTausen,)</td>\n",
       "      <td>(2D, multispectral, plant-phenotyping, climate...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mapreader-plant</td>\n",
       "      <td>automated detection of plant patches in images...</td>\n",
       "      <td>(TaskEnum.classificiation, TaskEnum.object_det...</td>\n",
       "      <td>https://github.com/alan-turing-institute/mapre...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/m...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(2D, plant, phenotype, rgb, biology, agriculture)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>resnet50-plankton</td>\n",
       "      <td>automated classification of plankton images co...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/plank...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/p...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(alan-turing-institute, cefas, plankton-analyt...</td>\n",
       "      <td>(2D, plankton, ecology, environmental-science)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>vedge-detector</td>\n",
       "      <td>automated detection of coastal vegetation edge...</td>\n",
       "      <td>(TaskEnum.segmentation,)</td>\n",
       "      <td>https://github.com/MartinSJRogers/VEdge_Detect...</td>\n",
       "      <td>git+https://github.com/MartinSJRogers/VEdge_De...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(cambridge-university, birkbeck-university)</td>\n",
       "      <td>(2D, satellite, remote-sensing, ecology, envir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>image-classifiers</td>\n",
       "      <td>Scivision plugin for the image-classifiers pac...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(classification, 2D, image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>huggingface-classifiers</td>\n",
       "      <td>Selection of image classifiers taken from http...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(alan-turing-institute, huggingface)</td>\n",
       "      <td>(classification, 2D, image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>huggingface-segmentation</td>\n",
       "      <td>Selection of segmentation algorithms taken fro...</td>\n",
       "      <td>(TaskEnum.segmentation,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(alan-turing-institute, huggingface)</td>\n",
       "      <td>()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>huggingface-object-detection</td>\n",
       "      <td>Selection of object detection algorithms taken...</td>\n",
       "      <td>(TaskEnum.object_detection,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(alan-turing-institute, huggingface)</td>\n",
       "      <td>()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>detectreeRGB-forest</td>\n",
       "      <td>Detect changes in tall tree height with machin...</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
       "      <td>https://github.com/shmh40/detectreeRGB-treecro...</td>\n",
       "      <td>git+https://github.com/shmh40/detectreeRGB-tre...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>(cambridge-university,)</td>\n",
       "      <td>(2D, satellite, remote-sensing, ecology, envir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>flower-classification-model</td>\n",
       "      <td>Identify flower species</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/flowe...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/f...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(flowers, ecology)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>butterfly-classification-model</td>\n",
       "      <td>Identify butterfly species</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/butte...</td>\n",
       "      <td>git+https://github.com/alan-turing-institute/b...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(butterflies, insects, animals, ecology)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name  \\\n",
       "0                         stardist   \n",
       "1                          PlantCV   \n",
       "2                      greenotyper   \n",
       "3                  mapreader-plant   \n",
       "4                resnet50-plankton   \n",
       "5                   vedge-detector   \n",
       "6                image-classifiers   \n",
       "7          huggingface-classifiers   \n",
       "8         huggingface-segmentation   \n",
       "9     huggingface-object-detection   \n",
       "10             detectreeRGB-forest   \n",
       "11     flower-classification-model   \n",
       "12  butterfly-classification-model   \n",
       "\n",
       "                                          description  \\\n",
       "0   Single class object detection and segementatio...   \n",
       "1   Open-source image analysis software package ta...   \n",
       "2   Greenotyper is an image analysis tool for larg...   \n",
       "3   automated detection of plant patches in images...   \n",
       "4   automated classification of plankton images co...   \n",
       "5   automated detection of coastal vegetation edge...   \n",
       "6   Scivision plugin for the image-classifiers pac...   \n",
       "7   Selection of image classifiers taken from http...   \n",
       "8   Selection of segmentation algorithms taken fro...   \n",
       "9   Selection of object detection algorithms taken...   \n",
       "10  Detect changes in tall tree height with machin...   \n",
       "11                            Identify flower species   \n",
       "12                         Identify butterfly species   \n",
       "\n",
       "                                                tasks  \\\n",
       "0   (TaskEnum.object_detection, TaskEnum.segmentat...   \n",
       "1   (TaskEnum.segmentation, TaskEnum.thresholding,...   \n",
       "2                            (TaskEnum.segmentation,)   \n",
       "3   (TaskEnum.classificiation, TaskEnum.object_det...   \n",
       "4                         (TaskEnum.classificiation,)   \n",
       "5                            (TaskEnum.segmentation,)   \n",
       "6                         (TaskEnum.classificiation,)   \n",
       "7                         (TaskEnum.classificiation,)   \n",
       "8                            (TaskEnum.segmentation,)   \n",
       "9                        (TaskEnum.object_detection,)   \n",
       "10  (TaskEnum.object_detection, TaskEnum.segmentat...   \n",
       "11                        (TaskEnum.classificiation,)   \n",
       "12                        (TaskEnum.classificiation,)   \n",
       "\n",
       "                                                  url  \\\n",
       "0                https://github.com/stardist/stardist   \n",
       "1           https://github.com/danforthcenter/plantcv   \n",
       "2          https://github.com/MarniTausen/Greenotyper   \n",
       "3   https://github.com/alan-turing-institute/mapre...   \n",
       "4   https://github.com/alan-turing-institute/plank...   \n",
       "5   https://github.com/MartinSJRogers/VEdge_Detect...   \n",
       "6   https://github.com/alan-turing-institute/scivi...   \n",
       "7   https://github.com/alan-turing-institute/scivi...   \n",
       "8   https://github.com/alan-turing-institute/scivi...   \n",
       "9   https://github.com/alan-turing-institute/scivi...   \n",
       "10  https://github.com/shmh40/detectreeRGB-treecro...   \n",
       "11  https://github.com/alan-turing-institute/flowe...   \n",
       "12  https://github.com/alan-turing-institute/butte...   \n",
       "\n",
       "                                              pkg_url format  \\\n",
       "0   git+https://github.com/stardist/stardist.git@main  image   \n",
       "1   git+https://github.com/danforthcenter/plantcv@...  image   \n",
       "2   git+https://github.com/MarniTausen/Greenotyper...  image   \n",
       "3   git+https://github.com/alan-turing-institute/m...  image   \n",
       "4   git+https://github.com/alan-turing-institute/p...  image   \n",
       "5   git+https://github.com/MartinSJRogers/VEdge_De...  image   \n",
       "6   git+https://github.com/alan-turing-institute/s...  image   \n",
       "7   git+https://github.com/alan-turing-institute/s...  image   \n",
       "8   git+https://github.com/alan-turing-institute/s...  image   \n",
       "9   git+https://github.com/alan-turing-institute/s...  image   \n",
       "10  git+https://github.com/shmh40/detectreeRGB-tre...  image   \n",
       "11  git+https://github.com/alan-turing-institute/f...  image   \n",
       "12  git+https://github.com/alan-turing-institute/b...  image   \n",
       "\n",
       "    scivision_usable  pretrained  labels_required  \\\n",
       "0              False        True             True   \n",
       "1              False        True             True   \n",
       "2              False        True             True   \n",
       "3               True        True            False   \n",
       "4               True        True            False   \n",
       "5               True        True            False   \n",
       "6               True        True            False   \n",
       "7               True        True            False   \n",
       "8               True        True            False   \n",
       "9               True        True            False   \n",
       "10              True        True            False   \n",
       "11              True        True             True   \n",
       "12              True        True             True   \n",
       "\n",
       "                                          institution  \\\n",
       "0                                             (epfl,)   \n",
       "1                                   (danforthcenter,)   \n",
       "2                                      (MarniTausen,)   \n",
       "3                            (alan-turing-institute,)   \n",
       "4   (alan-turing-institute, cefas, plankton-analyt...   \n",
       "5         (cambridge-university, birkbeck-university)   \n",
       "6                            (alan-turing-institute,)   \n",
       "7                (alan-turing-institute, huggingface)   \n",
       "8                (alan-turing-institute, huggingface)   \n",
       "9                (alan-turing-institute, huggingface)   \n",
       "10                            (cambridge-university,)   \n",
       "11                           (alan-turing-institute,)   \n",
       "12                           (alan-turing-institute,)   \n",
       "\n",
       "                                                 tags  \n",
       "0   (2D, 3D, optical-microscopy, xray, microtomogr...  \n",
       "1   (2D, hyperspectral, multispectral, near-infrar...  \n",
       "2   (2D, multispectral, plant-phenotyping, climate...  \n",
       "3   (2D, plant, phenotype, rgb, biology, agriculture)  \n",
       "4      (2D, plankton, ecology, environmental-science)  \n",
       "5   (2D, satellite, remote-sensing, ecology, envir...  \n",
       "6                         (classification, 2D, image)  \n",
       "7                         (classification, 2D, image)  \n",
       "8                                                  ()  \n",
       "9                                                  ()  \n",
       "10  (2D, satellite, remote-sensing, ecology, envir...  \n",
       "11                                 (flowers, ecology)  \n",
       "12           (butterflies, insects, animals, ecology)  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compatible_models = default_catalog.compatible_models(\"scivision-test-data\").to_dataframe()\n",
    "compatible_models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `huggingface-classifiers` catalog entry can be used to load some of the most popular image classification models from [Hugging Face](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads). See the list of included models in the [model repo](https://github.com/alan-turing-institute/scivision_huggingface). As before, let's load one of the named models and run it on our test image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ifenton/opt/anaconda3/envs/scivision_comp/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /Users/runner/miniforge3/conda-bld/pytorch-recipe_1675740371165/work/aten/src/ATen/native/TensorShape.cpp:3191.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
     ]
    }
   ],
   "source": [
    "huggingface_repo = models_catalog[models_catalog.name == \"huggingface-classifiers\"].url.item()\n",
    "microsoft_model = load_pretrained_model(huggingface_repo, model_selection='microsoft_swin_tiny_patch4_window7_224', allow_install=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Predicted class: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microsoft_model.predict(test_image)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}