how-to-use-scivision.ipynb

{
 "cells": [
  {
   "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"
   ]
  },
  {
   "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"
   ]
  },
  {
   "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": [
       "AnyUrl('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": [
    {
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       "  <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>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>8</th>\n",
       "      <td>image-classifiers</td>\n",
       "      <td>None</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>False</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(classification, 2D, image)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                name description                        tasks  \\\n",
       "8  image-classifiers        None  (TaskEnum.classificiation,)   \n",
       "\n",
       "                                                 url  \\\n",
       "8  https://github.com/alan-turing-institute/scivi...   \n",
       "\n",
       "                                             pkg_url format  pretrained  \\\n",
       "8  git+https://github.com/alan-turing-institute/s...  image        True   \n",
       "\n",
       "   labels_required               institution                         tags  \n",
       "8            False  (alan-turing-institute,)  (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\"]"
   ]
  },
  {
   "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\" 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": [],
   "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='resnet18')\n",
    "densenet_model = load_pretrained_model(model_repo, model='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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later, we'll use these models to make predictions on image data found in the scivision catalog."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query the default scivision data catalog"
   ]
  },
  {
   "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": {
    "scrolled": false
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   "outputs": [
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>tasks</th>\n",
       "      <th>domains</th>\n",
       "      <th>url</th>\n",
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       "      <th>institution</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data-002</td>\n",
       "      <td>None</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>(oceanography,)</td>\n",
       "      <td>https://github.com/scotthosking/intake-plankto...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(CEFAS,)</td>\n",
       "      <td>(help-needed, 2D)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data-003</td>\n",
       "      <td>Koala</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.classific...</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
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       "      <td>(Wikipedia by Erik Veland,)</td>\n",
       "      <td>(demo,)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data-004</td>\n",
       "      <td>Sample dataset of Plankton required to demonst...</td>\n",
       "      <td>(TaskEnum.classificiation,)</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://raw.githubusercontent.com/alan-turing-...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(Centre for Environment, Fisheries and Aquacul...</td>\n",
       "      <td>(2D, plankton, ecology, environmental-science)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>data-005</td>\n",
       "      <td>Sample dataset of satellite imagery required t...</td>\n",
       "      <td>(TaskEnum.classificiation, TaskEnum.segmentation)</td>\n",
       "      <td>(computer-vision, earth-observation, ecology)</td>\n",
       "      <td>https://github.com/MartinSJRogers/VEdge_Detect...</td>\n",
       "      <td>image</td>\n",
       "      <td>False</td>\n",
       "      <td>(Cambridge University,)</td>\n",
       "      <td>(2D, satellite, remote-sensing, ecology, envir...</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       name                                        description  \\\n",
       "2  data-002                                               None   \n",
       "3  data-003                                              Koala   \n",
       "4  data-004  Sample dataset of Plankton required to demonst...   \n",
       "5  data-005  Sample dataset of satellite imagery required t...   \n",
       "\n",
       "                                               tasks  \\\n",
       "2                        (TaskEnum.classificiation,)   \n",
       "3  (TaskEnum.object_detection, TaskEnum.classific...   \n",
       "4                        (TaskEnum.classificiation,)   \n",
       "5  (TaskEnum.classificiation, TaskEnum.segmentation)   \n",
       "\n",
       "                                         domains  \\\n",
       "2                                (oceanography,)   \n",
       "3                             (computer-vision,)   \n",
       "4                             (computer-vision,)   \n",
       "5  (computer-vision, earth-observation, ecology)   \n",
       "\n",
       "                                                 url format  labels_provided  \\\n",
       "2  https://github.com/scotthosking/intake-plankto...  image             True   \n",
       "3  https://github.com/alan-turing-institute/scivi...  image             True   \n",
       "4  https://raw.githubusercontent.com/alan-turing-...  image             True   \n",
       "5  https://github.com/MartinSJRogers/VEdge_Detect...  image            False   \n",
       "\n",
       "                                         institution  \\\n",
       "2                                           (CEFAS,)   \n",
       "3                        (Wikipedia by Erik Veland,)   \n",
       "4  (Centre for Environment, Fisheries and Aquacul...   \n",
       "5                            (Cambridge University,)   \n",
       "\n",
       "                                                tags  \n",
       "2                                  (help-needed, 2D)  \n",
       "3                                            (demo,)  \n",
       "4     (2D, plankton, ecology, environmental-science)  \n",
       "5  (2D, satellite, remote-sensing, ecology, envir...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compatible_datasources = default_catalog.compatible_datasources(\"image-classifiers\").to_dataframe()\n",
    "compatible_datasources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `data-003`, an image dataset containing a single image of a Koala."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>tasks</th>\n",
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       "      <th>format</th>\n",
       "      <th>labels_provided</th>\n",
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       "      <th>tags</th>\n",
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       "      <th>3</th>\n",
       "      <td>data-003</td>\n",
       "      <td>Koala</td>\n",
       "      <td>(TaskEnum.object_detection, TaskEnum.classific...</td>\n",
       "      <td>(computer-vision,)</td>\n",
       "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
       "      <td>image</td>\n",
       "      <td>True</td>\n",
       "      <td>(Wikipedia by Erik Veland,)</td>\n",
       "      <td>(demo,)</td>\n",
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      "text/plain": [
       "       name description                                              tasks  \\\n",
       "3  data-003       Koala  (TaskEnum.object_detection, TaskEnum.classific...   \n",
       "\n",
       "              domains                                                url  \\\n",
       "3  (computer-vision,)  https://github.com/alan-turing-institute/scivi...   \n",
       "\n",
       "  format  labels_provided                  institution     tags  \n",
       "3  image             True  (Wikipedia by Erik Veland,)  (demo,)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "target_datasource = compatible_datasources.loc[compatible_datasources['name'] == 'data-003']\n",
    "target_datasource"
   ]
  },
  {
   "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()"
   ]
  },
  {
   "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": "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/scivision/lib/python3.9/site-packages/xarray/core/dataset.py:2172: FutureWarning: None value for 'chunks' is deprecated. It will raise an error in the future. Use instead '{}'\n",
      "  warnings.warn(\n"
     ]
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       "  --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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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-61ae4780-9211-4db7-9f03-5572c335ad27' class='xr-array-in' type='checkbox' checked><label for='section-61ae4780-9211-4db7-9f03-5572c335ad27' 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>\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> Count </th>\n",
       "                        <td> 1 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> uint8 </td>\n",
       "                    <td> 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-ff523442-fab8-46c9-900f-d1b6cb8542f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff523442-fab8-46c9-900f-d1b6cb8542f3' 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-13780d36-e9c9-40a5-bcfc-b109908f353d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13780d36-e9c9-40a5-bcfc-b109908f353d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e21c38d-de6c-4290-8024-61f15691b019' class='xr-var-data-in' type='checkbox'><label for='data-4e21c38d-de6c-4290-8024-61f15691b019' 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-d92070bf-195a-4d4b-a21a-fa96d02cf14e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d92070bf-195a-4d4b-a21a-fa96d02cf14e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbaac594-10c9-41fc-8024-2f68ee959ce9' class='xr-var-data-in' type='checkbox'><label for='data-dbaac594-10c9-41fc-8024-2f68ee959ce9' 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-4887cbb1-67c0-4df3-93c6-d051111f18ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4887cbb1-67c0-4df3-93c6-d051111f18ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-addf3f74-850e-457d-8255-bf4477f509ad' class='xr-var-data-in' type='checkbox'><label for='data-addf3f74-850e-457d-8255-bf4477f509ad' 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-cb918f85-702d-44e3-aca7-4535f1d568fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb918f85-702d-44e3-aca7-4535f1d568fc' 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": 11,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the image with `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16f282820>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_image)"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 462ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'koala : 99.78%'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet_model.predict(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'koala : 99.73%'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "densenet_model.predict(test_image)"
   ]
  },
  {
   "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!"
   ]
  },
  {
   "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": 16,
   "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>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>model-000</td>\n",
       "      <td>None</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>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>model-001</td>\n",
       "      <td>None</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>True</td>\n",
       "      <td>True</td>\n",
       "      <td>(danforthcenter,)</td>\n",
       "      <td>(2D, hyperspectral, multispectral, near-infrar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>scivision-test-plugin</td>\n",
       "      <td>None</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>False</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(dummy,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>False</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(2D, plant, phenotype, rgb, biology, agriculture)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>resnet50-plantkton</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>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>8</th>\n",
       "      <td>image-classifiers</td>\n",
       "      <td>None</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>False</td>\n",
       "      <td>(alan-turing-institute,)</td>\n",
       "      <td>(classification, 2D, image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>huggingface-classifiers</td>\n",
       "      <td>None</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>False</td>\n",
       "      <td>(alan-turing-institute, huggingface)</td>\n",
       "      <td>(classification, 2D, image)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>huggingface-object-detection</td>\n",
       "      <td>None</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>False</td>\n",
       "      <td>(alan-turing-institute, huggingface)</td>\n",
       "      <td>()</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name  \\\n",
       "0                      model-000   \n",
       "1                      model-001   \n",
       "4          scivision-test-plugin   \n",
       "5                mapreader-plant   \n",
       "6             resnet50-plantkton   \n",
       "8              image-classifiers   \n",
       "9        huggingface-classifiers   \n",
       "11  huggingface-object-detection   \n",
       "\n",
       "                                          description  \\\n",
       "0                                                None   \n",
       "1                                                None   \n",
       "4                                                None   \n",
       "5   automated detection of plant patches in images...   \n",
       "6   automated classification of plankton images co...   \n",
       "8                                                None   \n",
       "9                                                None   \n",
       "11                                               None   \n",
       "\n",
       "                                                tasks  \\\n",
       "0   (TaskEnum.object_detection, TaskEnum.segmentat...   \n",
       "1   (TaskEnum.segmentation, TaskEnum.thresholding,...   \n",
       "4                        (TaskEnum.object_detection,)   \n",
       "5   (TaskEnum.classificiation, TaskEnum.object_det...   \n",
       "6                         (TaskEnum.classificiation,)   \n",
       "8                         (TaskEnum.classificiation,)   \n",
       "9                         (TaskEnum.classificiation,)   \n",
       "11                       (TaskEnum.object_detection,)   \n",
       "\n",
       "                                                  url  \\\n",
       "0                https://github.com/stardist/stardist   \n",
       "1           https://github.com/danforthcenter/plantcv   \n",
       "4   https://github.com/alan-turing-institute/scivi...   \n",
       "5   https://github.com/alan-turing-institute/mapre...   \n",
       "6   https://github.com/alan-turing-institute/plank...   \n",
       "8   https://github.com/alan-turing-institute/scivi...   \n",
       "9   https://github.com/alan-turing-institute/scivi...   \n",
       "11  https://github.com/alan-turing-institute/scivi...   \n",
       "\n",
       "                                              pkg_url format  pretrained  \\\n",
       "0   git+https://github.com/stardist/stardist.git@main  image        True   \n",
       "1   git+https://github.com/danforthcenter/plantcv@...  image        True   \n",
       "4   git+https://github.com/alan-turing-institute/s...  image        True   \n",
       "5   git+https://github.com/alan-turing-institute/m...  image        True   \n",
       "6   git+https://github.com/alan-turing-institute/p...  image        True   \n",
       "8   git+https://github.com/alan-turing-institute/s...  image        True   \n",
       "9   git+https://github.com/alan-turing-institute/s...  image        True   \n",
       "11  git+https://github.com/alan-turing-institute/s...  image        True   \n",
       "\n",
       "    labels_required                                        institution  \\\n",
       "0              True                                            (epfl,)   \n",
       "1              True                                  (danforthcenter,)   \n",
       "4             False                           (alan-turing-institute,)   \n",
       "5             False                           (alan-turing-institute,)   \n",
       "6             False  (alan-turing-institute, cefas, plankton-analyt...   \n",
       "8             False                           (alan-turing-institute,)   \n",
       "9             False               (alan-turing-institute, huggingface)   \n",
       "11            False               (alan-turing-institute, huggingface)   \n",
       "\n",
       "                                                 tags  \n",
       "0   (2D, 3D, optical-microscopy, xray, microtomogr...  \n",
       "1   (2D, hyperspectral, multispectral, near-infrar...  \n",
       "4                                            (dummy,)  \n",
       "5   (2D, plant, phenotype, rgb, biology, agriculture)  \n",
       "6      (2D, plankton, ecology, environmental-science)  \n",
       "8                         (classification, 2D, image)  \n",
       "9                         (classification, 2D, image)  \n",
       "11                                                 ()  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compatible_models = default_catalog.compatible_models(\"data-003\").to_dataframe()\n",
    "compatible_models"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/scivision/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /Users/distiller/project/pytorch/aten/src/ATen/native/TensorShape.cpp:2228.)\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='microsoft_swin_tiny_patch4_window7_224', allow_install=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Predicted class: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microsoft_model.predict(test_image)"
   ]
  }
 ],
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