GLAMI_1M_Hands_On_Colab.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Hands-on Machine Learning with Multilingual Image-Text Fashion Dataset\n",
    "\n",
    "*Experiment with Zero-shot CLIP classifiction and image search with the largest multilingual image-text classification dataset and benchmark GLAMI-1M.*\n",
    "\n",
    "\n",
    "![GLAMI-1M Image](https://raw.githubusercontent.com/glami/glami-1m/main/media/glami-1m-dataset-examples.png)\n",
    "\n",
    "\n",
    "**Description**\n",
    "Come, see, and try working with over one million fashion products from the [GLAMI-1M](https://github.com/glami/glami-1m) dataset using modern techniques. For each product, this dataset contains an image, item description in one of 13 languages, and a category label (191 classes). You will learn how to use the popular CLIP model for zero-shot classification and image search.\n",
    "\n",
    "Whether you are just starting or an expert in machine learning, come to discover this new real-world dataset in a fun way.\n",
    "\n",
    "**Speaker**\n",
    "[Vaclav Kosar](https://vaclavkosar.com/), Machine Learning Engineer @ [GLAMI](https://glami.group/)\n",
    "\n",
    "**Venue**\n",
    "This event takes place on-line (link is in the sidebar) and off-line:\n",
    "GLAMI, Corso 2a, Křižíkova 148/34, 186 00, Prague 8. Reception desk will have all the necessary information.\n",
    "\n",
    "**Recording**\n",
    "The recording is [available on Youtube here](https://youtu.be/jLFJObPWU1U).\n",
    "\n",
    "**Questions** Please don't hesitate to ask any questions you may have, this is an open forum and we encourage participation to accelerate learning.\n"
   ],
   "metadata": {
    "id": "muYwlSwR7Vdc"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "## GLAMI-1M Dataset\n",
    "\n",
    "GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents **a difficult production industry problem**.\n",
    "\n",
    "![](https://github.com/glami/glami-1m/blob/main/media/glami-1m-dataset-examples-table.png?raw=true)\n",
    "\n",
    "Each sample contains an **image, country code, name in corresponding language, description, target category and source of the label** which can be of multiple types, it can be human or rule-based but most of the samples are **human-based labels**.\n",
    "\n",
    "![](https://raw.githubusercontent.com/glami/glami-1m/main/media/train-test-distribution-per-category.png)\n",
    "\n",
    "\n",
    "In GLAMI-1M, small amount of categories form the large portion of the data set. Meaning, that the distribution of product items across the product categories is highly long-tailed, which is a common problem in industrial data.\n",
    "\n",
    "\n",
    "![](https://raw.githubusercontent.com/glami/glami-1m/main/media/train-test-distribution-per-geo.png)\n",
    "\n",
    "In addition, above we can see distribution across different languages. There is over-representation in certain languages, but we see much less inbalance compared to the category distribution.\n",
    "\n",
    "GLAMI-1M is the largest multilingual image text classification dataset. It is the second largest image text classification dataset. It is second to Recipe1M+ which contains only English. Contrary to Recipe1M+, in GLAMI-1M we have more languages and more human labels. In fashion domain, GLAMI-1M is larger and has more categories than FashionGen dataset.\n",
    "\n",
    "GLAMI-1M is indeed smaller than open source webscape dataset like LAION-5B, but those are not suitable for classification task. In this notebook, we experiment with M-CLIP model trained on LAION-5B.\n",
    "\n",
    "**Paper Authors:**\n",
    "The GLAMI-1M paper authors are Vaclav Kosar, Antonin Hoskovec, Milan Šulc and Radek Bartizal. The funding institutions are [GLAMI](https://glami.group/), Rossum, Czech Technical University, Faculty of Science and Nuclear Engineering. This paper was published at British Machine Vision Conference 2022 (BMVC).\n",
    "\n",
    "\n",
    "**More Information:**\n",
    "- [GLAMI-1M Code](https://github.com/glami/glami-1m), [GLAMI-1M Data Files](https://huggingface.co/datasets/glami/glami-1m)\n",
    "- [GLAMI-1M Paper](https://bmvc2022.mpi-inf.mpg.de/0607.pdf), [Supplementary](https://docs.google.com/viewer?url=https://github.com/glami/glami-1m/raw/main/media/GLAMI-1M%20Supplementary.pdf), [Poster](https://bmvc2022.mpi-inf.mpg.de/0607_poster.pdf)\n",
    "- [GLAMI-1M Blog Post](https://medium.com/@glami-engineering/glami-1m-multilingual-image-text-fashion-dataset-a72691b6fedb)\n",
    "- [GLAMI-1M Benchmark at Papers with Code](https://paperswithcode.com/paper/glami-1m-a-multilingual-image-text-fashion-1)\n",
    "- [GLAMI-1M Hands-On Meetup Event](https://www.meetup.com/miton-times/events/291311521/)\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "id": "Fk7MvHeJQ6Sc"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Encoding Images, Texts, and Categories into Similar Numbers\n",
    "\n",
    "![](https://github.com/glami/glami-1m/blob/main/media/multilingual-zero-shot-classification.drawio.png?raw=true)\n",
    "\n",
    "Machine learning models can **turn image bits in your computer into a special array of numbers called vector embedding**. You can image that as if the image was translated from bytes for display into a point on a paper, so 2D. Or point in a room, so 3D. Or truer yet, a point in a space with **512 dimensions**.\n",
    "\n",
    "We can **do the same thing for text**. So that means we can take any texts in any language and map them to **the same sized array of numbers as for image**. And this array we can compare these vectors.\n",
    "\n",
    "Now, just turning things into points in space is not useful by itself, but **thanks to powerful computers and data from the internet**, we can find useful mapping from text or images to a points in space, such that certain **semantic similarities are preserved**. So similar images in a human sense will have these **points close together**. The same texts of similar meaning for humans.\n",
    "\n",
    "Finding this mapping, we call training a machine learning model, as if to learn to translate texts and images into points while preserving meaning.\n",
    "\n",
    "We can use this for finding similarities, similar images, similar products. And we can **also use this to find similarities between products and categories**. So we can find a category where a product belongs. How would you do that? Well, product category is a name, and the name of the category represents a text which needs to be similar semantically to the product itself. So if the product belongs to the category, the text of the category should be close to the product itself.\n",
    "\n",
    "What we are using here is something called **multimodality**. It means that we have a model that is capable of understanding more than one modality of input. It can work with different types of inputs: images, texts, and other. These kinds of models are increasingly popular, because we often deal with multimodal data as the **two sensory inputs complement each other**.\n",
    "\n",
    "Another feature we may want is dealing with multiple languages. We may want to be able to have **a symmetric solution, symmetric systems across different languages** and countries. For this, we need to be able to **encode text regardless of the language** the same way.\n"
   ],
   "metadata": {
    "id": "PZt9A2cVr7Rx"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Make a Copy of This Notebook\n",
    "If you would like to follow along and execute this notebook, I recommend to make a copy of this notebook into you Google Drive for future viewing.\n",
    "\n",
    "To execute this notebook you need a GPU runtime: Runtime -> Change Runtime -> GPU or TPU. If you cannot get the accelerator, try changing the device below and execute without, but that was not tested."
   ],
   "metadata": {
    "id": "TCCHNlB8jGVg"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "device = 'cuda'\n",
    "# device = 'cpu'"
   ],
   "metadata": {
    "id": "0S9w7iFpj-Nu"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Setting up the environment\n",
    "Install GLAMI-1M Repository and packages, later restart and rerun."
   ],
   "metadata": {
    "id": "nehWfXNT8sNo"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZHFp2vC2jdLQ"
   },
   "outputs": [],
   "source": [
    "# Ignore this or search what it is.\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "if os.environ.get('original_working_dir') is None:\n",
    "  os.environ['original_working_dir'] = os.getcwd() + '/'\n",
    "\n",
    "os.chdir(os.environ['original_working_dir'])\n",
    "!rm -rf 'glami-1m'\n",
    "!git clone https://github.com/glami/glami-1m.git\n",
    "os.chdir(os.environ['original_working_dir'] + '/glami-1m')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zpdakNjhw00L",
    "outputId": "1eb3571d-842e-43d0-876e-0010810242f3"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cloning into 'glami-1m'...\n",
      "remote: Enumerating objects: 332, done.\u001B[K\n",
      "remote: Counting objects: 100% (164/164), done.\u001B[K\n",
      "remote: Compressing objects: 100% (82/82), done.\u001B[K\n",
      "remote: Total 332 (delta 108), reused 129 (delta 81), pack-reused 168\u001B[K\n",
      "Receiving objects: 100% (332/332), 4.26 MiB | 15.32 MiB/s, done.\n",
      "Resolving deltas: 100% (192/192), done.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -r requirements.txt"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "g2VBVTeIK7ul",
    "outputId": "a4e9c1b5-19cf-4cb1-9b88-8bf9149ce10f"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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      "Building wheels for collected packages: clip, sacremoses\n",
      "  Building wheel for clip (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
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      "      Successfully uninstalled matplotlib-3.2.2\n",
      "  Attempting uninstall: scikit-image\n",
      "    Found existing installation: scikit-image 0.18.3\n",
      "    Uninstalling scikit-image-0.18.3:\n",
      "      Successfully uninstalled scikit-image-0.18.3\n",
      "  Attempting uninstall: tensorboard\n",
      "    Found existing installation: tensorboard 2.9.1\n",
      "    Uninstalling tensorboard-2.9.1:\n",
      "      Successfully uninstalled tensorboard-2.9.1\n",
      "  Attempting uninstall: tensorflow\n",
      "    Found existing installation: tensorflow 2.9.2\n",
      "    Uninstalling tensorflow-2.9.2:\n",
      "      Successfully uninstalled tensorflow-2.9.2\n",
      "Successfully installed clip-1.0 einops-0.6.0 einops-exts-0.0.4 ema-pytorch-0.1.4 ftfy-6.1.1 huggingface-hub-0.0.12 imagen-pytorch-0.10.0 keras-2.8.0 lightning-utilities-0.6.0.post0 matplotlib-3.3.3 multilingual-clip-1.0.10 numpy-1.22.4 pandas-1.4.3 pytorch-ignite-0.4.9 pytorch-lightning-1.9.0 pytorch-warmup-0.1.1 resize-right-0.0.2 sacremoses-0.0.53 scann-1.2.6 scikit-image-0.19.3 scipy-1.10.0 sentencepiece-0.1.95 tabulate-0.8.9 tensorboard-2.8.0 tensorflow-2.8.4 tensorflow-estimator-2.8.0 tokenizers-0.10.3 torchmetrics-0.11.1 tqdm-4.64.0 transformers-4.8.1\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "matplotlib",
         "mpl_toolkits",
         "numpy"
        ]
       }
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Now restart the kernel and re-run from start!\n",
    "We need to restart here because the Python kernel loaded new dependencies, meaning that we loaded new libraries and we need to restart the Python executor.\n",
    "\n",
    "Of course, don't loop yourself here for ever 😀.\n",
    "\n"
   ],
   "metadata": {
    "id": "Kss1VOHYDcfy"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Download, Load, and Sample From GLAMI-1M Test Set\n",
    "\n",
    "Now, we will download the dataset itself. We will not download the full version because it would take 30 minutes to download. Instead, we will download only a smaller test set, which contain 100k products.\n",
    "\n",
    "The datasets in general are always split into the training set and testing set. The training set is used for training only while the testing set is used for evaluation. So we keep the testing set apart. Model never sees the testing set before it is only evaluated on it. We test the generalization properties of models doing this.\n"
   ],
   "metadata": {
    "id": "_WDaXwAF3Xjc"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "import load_dataset;\n",
    "\n",
    "\n",
    "os.environ['EXTRACT_DIR'] = \"/tmp/GLAMI-1M/\"\n",
    "\n",
    "# Full dataset from takes 30 minutes to download.\n",
    "# load_dataset.download_dataset(dataset_url=\"https://huggingface.co/datasets/glami/glami-1m/resolve/main/GLAMI-1M-dataset.zip\")\n",
    "\n",
    "# Test set only is fast! Download in 30 to 90 seconds!\n",
    "load_dataset.download_dataset(dataset_url=\"https://huggingface.co/datasets/glami/glami-1m/resolve/main/GLAMI-1M-dataset--test-only.zip\")\n",
    "\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d_4e_PkiklCi",
    "outputId": "b2a4670b-e2ba-4f71-8466-b049f0c8e3f2"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Downloading: 100%|██████████| 1.39G/1.39G [01:16<00:00, 18.1MB/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Unzipping\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 116008/116008 [00:23<00:00, 4897.90it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now let's display the data set itself. We have it downloaded on our local machine. And now we can use functions from the GLAMI-1m repository to load a Pandas data frame into a table.\n",
    "\n",
    "Let's sample from the testing set for a purpose of experimentation in this notebook. We cannot display full 100k products, and so we will experiment with 10 products only.\n"
   ],
   "metadata": {
    "id": "h-75CLm2xDX2"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import load_dataset;\n",
    "\n",
    "\n",
    "df = load_dataset.get_dataframe('test')[['item_id', 'image_id', 'name', 'description', 'category_name', 'image_file']].copy()\n",
    "sample_df = df.sample(10, random_state=41)\n",
    "sample_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "8fV6tIoJ3AWr",
    "outputId": "2a46e7cd-c288-428a-8d83-5527fa587434"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
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       "        item_id  image_id                                               name  \\\n",
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       "80708    852217    749997                   Didelė Moteriška Piniginė Liu Jo   \n",
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       "\n",
       "                                              description  \\\n",
       "65725   Lacoste Сникърси L001 0321 1 Sma 7-42SMA009240...   \n",
       "61857   Jope tüüp: Lendurijope Kinnitamise tüüp: Tõmbl...   \n",
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       "46129                                                       \n",
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       "80566   Vlněné pánské spodky jsou vyrobeny z vysoce kv...   \n",
       "12911   White cotton fruit logo graphic T-shirt from M...   \n",
       "91321                                                       \n",
       "34030   Sudėtis:   46% medvilnė    38% poliesteris    ...   \n",
       "\n",
       "                       category_name  \\\n",
       "65725                  mens-sneakers   \n",
       "61857                 womens-bombers   \n",
       "5906                      mens-pants   \n",
       "100861  womens-loafers-and-moccasins   \n",
       "46129                        kitchen   \n",
       "80708                 womens-wallets   \n",
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       "\n",
       "                                              image_file  \n",
       "65725   /tmp/GLAMI-1M/GLAMI-1M-dataset/images/590722.jpg  \n",
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      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Above table is a little bit sad. That is because we cannot see the images. We can display the images using a function provided in the GLAMI-1M repository called public dataset to HTML.\n",
    "\n",
    "Below we can see different products with descriptions in different languages. So for example, we can see a sneaker, a bomber jacket, trousers, some loafer or moccasin shoe. Okay, so the data set actually contains 191 detailed categories. I may not know all of them. And I actually learned fair amount of about fashion categories developing for GLAMI. Okay, next item is a kitchen item, some forks. Then we have a wallet for women. Then we have a men's undergarment. It looks like a pants, but it's undergarment. Then they have a Carol's t-shirt and shirt. Then we have a men's t-shirt and tank top. Then we have a men's parka, which is a type of a jacket."
   ],
   "metadata": {
    "id": "lSxWQhFKxGMk"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from utils import public_dataset_to_html\n",
    "\n",
    "public_dataset_to_html(sample_df)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "oszidPI_3GR5",
    "outputId": "ed92ef49-8afa-42c3-e4f6-3c431d12a2d1"
   },
   "execution_count": null,
   "outputs": [
    {
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     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
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       "  <thead>\n",
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       "      <td>641871</td>\n",
       "      <td><img width=\"150\" 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tL7P6/GamPO2SJDR5sobOPcnSa1gHwnkXgbC+zh7p5W3hl7T2ScfeEz7He0MZ/yP7VVRiHEqR/s9ZENrPHMA8AePzHJYzxqabxm4eiBYaZGXtwI/BBkB24oyvaboMgNhc8FqEtUOLhZpIHgr27EdVRTggvae7248B6/JWIvuWjuB5yypmqprKDCcYpa2ame6OaNvEgG2oA/EwnmLhay08sOtM4545dm8/C5aamkp6LRLRvaWSUNQCYy07ENPFm3IXHgOKzGV912uQ1uXMm5cpKTD8LLe6B9joWvNQ9rid2tJJIAJ4jYcle0EXLHgWG5hp8yR5ixigfXVdWx8dont04ewbtbYkXLtwXC9iLcDcsMutyswlt8YqmESmChaf+2e9eB6gNB+au9KaDMv4cKk1M1L7TUc6ir+ukPlfYc+FlL8jfEcbYwCHOtwufyUuRp1OIUFFIGTzRMkd8MLfekd5Mbdx+StFrqDNGK09U1keHVHsjS3vPa3sjLGk2uGXLx4XABUiaKtO6Wsp6yLXBKHW4g7EeYWmWwgICAgICAgckEIxYh+MVetrXsc/SWv+E2FrHosrCPVWA0ktzSSmmf/ANuTdnof/KlNW41Th9dh79U8LmtB2kbu35hSleTdnoGV/pV5rwOO0dJi9O3EImDYaidRt6uet5dcYZwipl9ANdcLk6MrCAOSIyAi2yEQuBDQXOOyQkyBxe6/AD/ey19kbA0RwulmcyOJjS5znHS1oG5JJ4ABWMbLfNHa921yZilmyjk2eQYWT3dRWRAh9ab20M5iP8XeXH36WjX4snj1dW/ww9b7IOzmLImTRLiELHY3Xta+rcbHum8WwjwbxPV1+gXDV1d0/Z20sNsO9m/NWFZJy1NjGJzEsHuRU7T9ZPIeDI/E8+NhuvJnnjhjOWb6HA8DrcZrY6Ghjuyl4Vk5naN2ldp4zRS4xPgbaQ2fLTe9DCw/3RYdpSQANB2AueJJPz9HW1uI1N2HTGH7vmvKOV8i4L0eKj1OIy8TMbf0/wBX0FrzLSAXoKHEQOLoJzTvP3Xgt/1L6kS/nVeFY8VxZx0vyvikZ6menLfn3itoxRYjieIYaK+gpKN9K6MvZL7V7RrAv8LYgQ47WsHblA9kqax8UUuJukme1kr6JrvZC2ImxJa3VISOFiQL3BKi0yQU1HSRvgpqeOjNTMYWQzAQukA+Itc33nXALgSb+Sh2bL2EOcC2RhfK2Nhc3iBvcOZvY7/EeN+qisscUrZHSNvE7vS64O5HgRwQdKmxfEKdumR7agfxix+YV3JtdODHqSQWmDoT/FuPmFdzO2W9DWUlRtBUxSHo1wJ+S0jOgICAgIILUvc7EJ3WuHSu3HnzWW4phMbdJA28LXHyQpZ9bFfQbNJ3AGoEeXL8URw6vKOWMSzPS5kqcDjGK0YLYayjlMcmk391wFg5u52IKVcHZ2vZonO0wVFnDfu5RYrO1rctdHND/ascB+9xB9VmlibVY+/NBQyF7rC/6LXZI6sktTSYfh81fiFTFS0kDDJLNK7SxjRxc48grjjbMzTxnEsVzN25YrJgmWnT4PkmGTRVYlIzS+tIPwgcx0ZwHF3Jq9XTSi57uHXV6R2afZXkbL+OdrOIZkwygazLuX5BR4fr951VUM4zPcfiN7v6bsAsAtZ6kxhU95ZwwicrjtD2/MeY8Myzl+oxjFZxFTwtueZceTWjm48AP6rxZZRETlPaH0OG4fU4jUx0tLG8spqI8y+ecPp8y9t3aA6eskNHhVOdTiBcUsZ20s6yu6+ZOwAXx92XHZ12wh/VJ09D6O4KM6jLitSP5fH2j38y+jsEwbDMvYHBhGEUraekgbZjBuT1LjxLjxJPFfWwwxwjbHZ/LuJ4rV4rVy1tbK8p7y6AcLFbh5VQRZUUa1rWBrWgNA2AFgEGKopKaqp5IZ4mvZI3Q8bgkX4XFj+KDLbYjkpSwxxQRU8DYYI2RRMGlrGCwaOgCSq+ygtLQgtc0Bu+3mg1Jo45BvHrPI2tb1SOid29hONS4dIIK2Z8tMbAOcdRj8b8wt7k2pe1zXtDmkEEXBHNVlVAQUJs0noggRJ7xxdxJJWW+4iqEBBTS1w95oPmESlHRhzbX26O3H6hClGPmh+GRzWgWI+MH+ad0NdPI0GaLu7/AG4TceqUtyQ0g1FzJ45d+HA/is0tvL8Sy9mbtMzRJFmqkrcByhQT2iwt50T4i9p/tJCCbM6WPlvdy7RnGEdO7jsnOevZIu0DFqfJPYti9ThUEVI2npPZqOKFulrHSHu2hoHC2on0WNKN+cRLec7cZp0+zzL0eUuyzB8EawNmZTtkqCB8UrxreT6m3oFrUzuZlNPGopEe2vKmYMz4DRvwNhqTSzGR9HqAMo02BbewLgb+6TvfbovncdpZ6mntwfs/ovmXB8v46dbjOnSonxM+8/MdL9nC7CsNztg1fiNPieDS0WEygOPtjTFIZhsNAPEW+InYbWJOy8vLtPVwmYyioff+veP5ZxuGnloam/Vj+HrG37/7e/l7eIm3LpDrcedzt4DwX1n8ytcGAcHvH3iraUqNY4SH7wulpSodKOBaR5EKWUvD5LbtafJy1cJSoc8j4G/5ksAZCODB6koK2kP22Dyb/VBXujuXSvO3AG35KULCxjSS1ovfiiw15Xe6bqK0JveJA3J4IrrYLjr8LpfZ8Ts2nDgI3uduwE8LdFvG2JhMwbi4VZEFDYsIJsLIIAZe6ldDJZ2lxbe/jyWLbiF40OHuvsejksWua5u5CLagKlhyWhS9uCC0tBJO4PUbFEpZ3R3I0knmPcd8whS9tRPEbd5Ztvhkbt8xsiOPmvLeEZyy6MJxuKqhpxUR1OqjeAS6N12g7EFp5iy1hO2bhMouKlISYp/7Kdv+E7LMxKwt7mbe8eoeG6zUtRKxkYjJtEGE8SG2uotyzNOyqLr7IA4IKghBXUAgB+xQVElhyQo78An9UtIg71x2aHHyCtlLX6ranvawfxFNsltM1FKXujY5872i5bGNvmrGJbUmrJWWZqipHE/2bB3shHkP6rUREI0Z2PYDNI3uf/uqjrfvf4WDhwPRLE5yhiZrsMfSlznmk0MEjhYvaRsSOuxCJKRIgRcWKCIY5kd1ZO6swXE34fUucHOY9neQyEfvNPDzBBWZx8NRlSMVTcxYI4ftbA6h8AkJdVYdeoj0nmWj322PgVjrDUTEmGZhocQaPYa2KZxeWObG7dpF9nNO426hWJKdJlVFI0Os1wJIuNjccVbF943cHkHo4KBodxADh/CbqwLb8rIKhUVG3NGVO6ZqJA0k82myCroH21uDXdDIzceoVgUYHsuWiUeLJNQ+RSxkbUzRssah/nLER+SXAubVvLbF9JI7nZ2n80ihkEztFzTRk9GSXSoLk7w6bmjeD0DrqVBYJBpBNJKD0uDb8VdseS1BKNFzRzNJ5Ei/4FTbBajZiW3NDI3wc4fyKtQXK32hwDtVNEwDhqk4/okRAw/tDQ83nomNtwadTrp0GN1VPI8OE1VI0D4IotLT94/qljE6N2oVDoIWu+Fr55DI7fkGja/qtWMM1RG1lpaiado+y28UfoQN/ms2UwGok7lzKKFkTSbnumjgerjy8eZvwA3WsQ50rm9yWzSkk8m7n1JJ6dTyHW8pISXs7lJxitiYNMYgbccbnVsT+PzVhJh6IqggICDj4tlXLuOOD8UwilnlG7ZtGmRp6h4s4fNScYlYymEen7O3wN/5NmGthAk7wR1rRVNB6AmzgPUrOxre058IzTQk99hEVawf3lBOCbf4H6T8iVKlbhpOxKnglEVWZaKXlHWRmE+moAH0JQbrah2gaiHNPAncH1SxeHxEbtLfEG6ti6zbXEjSPkqi3vd9LOJvYnn5IUpFNoLfiOsfC7gfA9ERnIbp1sN23+Xggs73Q/S0AnibE7fJWxR87r7tY7bgW39bf1sliwPiMVzBAfHQ39bfiPVQWNdAXf8ATRWtyB39R/IEeKCjnQDYU434e8eHkDv57DxVsULqYN3p/Ee8dx5F34gkJYx66a//AEsVzu0G529Tc+YHzUsWulgF9FJTttxOkG3qRt679AixCprZWfCGRX+yG2B9HWPyKtlMBqaiR3uyyOc7f3Sbn/Ly8th1JUKYXN0u1SaGlw3Nhf1sAfQ2vzKKxPmiF3RxknYd482P4W+XAIlNSeqfJcF5dvwGwUtaaT3usd7DwS1p6B2cULo8Nq697CBM8MYTza0bkep/BXFiU3WmRAQEBAQEGOWCGeExTxMljdxY9ocD6FBH6nI2AyvdJRRzYZI7fVQyGMerN2n5LO2Goylw6vKeaaIF+HVuH4qwf3dS000p++27SfuhZnGVjKPdxajE6qgIjxrCK7Cnk6e8nZ3kN+R71l2287KX5a+G5TVcFTD31NMyZjiS1zHh4B5i4/3utRKM4de+k3uNTfP/AH+asFKsqNDwW3s6/LbyKJSpBcdTPeHG3Ej/AH6nyRFkhcPjaRvzHP15+hKCmp1rHVcjne5/M/n6IMYdu42va1z0Pj/Ug+SLC197OLmutzuPzuPzuixUKaZdFxG+199jx+X8vVE7Le7kPGwDuOp1vmN/xv6IWtcyzAe+aCOAGrb1vt6W9EVZ9UwEMD3X6WaPkOPqgxGdzGOa1+kHiG7l3n/VSxrOkP2QB4ncpY1p5I4YDPUStjjHGSVwa0ep2UWHFbmnL01T3EGLQTuvYmE6wPXgpEwPS8p4Hluvp21Zc6tlG+mX4R90cfVaiIYmZTxjGxsDGNDWgWAAsAFtlVAQEBAQEBAQEBBQta5pa4Ag8QUEdr8iZZr5u/FAaKo1au/oZHU779ToIB9QVmcYlqMpcSqyLjtMx5wfMcdR72pkeKU4dp8NcWk28wVNsrGXlx6qjzrQSuFTlM1kNrmbDKxkhaeoZJocQpU+FuPLi1WdqPDW/wDOsOxfCXtPvCvw6aNpHVrw0tPzWd3laX0naDk2sYDS5wwZ1/surGRu9WuII9QrGUFfZ1YMaoqht6XFaOYcu6qmP/IrVpTYFTK8e45zh/C66WKOkntu2TbzVsYZKgRi8r42f43hv5qWOXV5oy5QNJrsw4NTW49/XRMt83BLEYxHtp7KMM1Ct7RcuNcOLYapszvkzUpYiGJ/Sj7IaPUKXFsVxZw4NocPksfvSaAs7ohaQzEvpaU0gczLeQqyUn4ZMSq2xj/JGHH/AFKTmUilZ279rGOvLaWWiwiJ3BlBTDUPvyFx+VljfLUQ0YZM1Y3UibF66tr5DvqqZXSfK5sFlronGX8uY1LKwwwyg3HAFWLZmbfTHZdg2NUMTXVWsNtzXowc8ph66OC2wICAgICAgICAgICAgICChAIsRcdCg4uJ5NynjIIxbLWE1t+JnpWPJ9SFmcYn2WMpjtKFYp9HbsZxbUZ8h4bE4/apgYj/AKSszpYtRqZIjiH0PuyKrBNHFjGHuPDuKx1h6FT0o8r6k+EZr/oR5RmB9izjjcPQSEu/JwU9KfK+p5hHKr6CGHyOJjzkZAeU0Lj/APoqell5PUjw0P8A2EwNeXMzFQO84CP5KelPk3x4bUH0HBDt+3KIjwa4fyT0Z8nqQ61J9C+lhI7zFaV3k0/or6U+T1I8JDQ/RJwSmsZK+I26MKRok6iUYf8ARtyvR21z67dGLUaUMzmlWH9jeUaC1qcvI6gLWyE3SlFDlHAMPA9noIwRzIWoxhLdiOKOJumNjWjoAqi9AQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBB//Z\"></td>\n",
       "      <td>Сникърси Lacoste</td>\n",
       "      <td>Lacoste Сникърси L001 0321 1 Sma 7-42SMA0092407 Бял</td>\n",
       "      <td>mens-sneakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1259907</td>\n",
       "      <td><img width=\"150\" 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\"></td>\n",
       "      <td>ALPHA INDUSTRIES Kevad-sügisjope must</td>\n",
       "      <td>Jope tüüp: Lendurijope Kinnitamise tüüp: Tõmblukk Muster: Ühevärviline Disain: Külgmised sisseõmmeldud taskud, Sirge lõige, Ülemõõduline, Soonikkätised, Riba-   kootud soonikkrae Materjal: Kunstkarv Rinnaga toitmine: Kergelt vooderdatud Lisad: Pehme materjal, Toon toonis õmblused, Vetthülgav Istuvus: Lõtv tegumood</td>\n",
       "      <td>womens-bombers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>318452</td>\n",
       "      <td><img width=\"150\" 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\"></td>\n",
       "      <td>Pánské lyžařské kalhoty KILPI RHEA-M červená</td>\n",
       "      <td>Kolekcia 2019   Pánske lyžiarske softshellové nohavice Kilpi RHEA-M s predtvarovanými kolenami pre lepšiu voľnosť pohybu. Nohavice majú dve hlavné vrecká na zipsy a jedno zadné vrecko na zips. Nohavice u nohavice sú rozopínajúce a majú snehové manžety. Nohavice sú tiež vybavené ochranou proti okopaniu. Pas sa dá regulovať suchým zipsom, zapínanie nohavíc je na zips a na dva patenty, traky odnímateľné. Priedušný a vodeodpudivý materiál Siberium 10 000 SRC SS     materiál Siberium 10.000 SRC SS vodeodolnosť 10 000 mm   cm2, priedušnosť 10.000 g   m2   24h   predtvarované kolená   2 hlavné vrecká na zips, 1 vrecko vzadu, tiež na zips   rozopínajúce nohavice s ochranou proti okopaniu   nastaviteľný pás pomocou suchého zipsu   odnímateľné traky</td>\n",
       "      <td>mens-pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493274</td>\n",
       "      <td><img width=\"150\" src=\"data:image/png;base64,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\"></td>\n",
       "      <td>MUSTANG Slip-on σκούρο μπλε</td>\n",
       "      <td>Μύτη παπουτσιού: Στρόγγυλη μύτη Είδος κλεισίματος: Παντοφλέ Σχέδιο: Μονόχρωμα Σχεδιασμός: Διακοσμητική ραφή, Ελαστικό ένθεμα, Ενισχυμένη φτέρνα, Τρακτερωτή σόλα Υλικό: Ύφασμα, Απομίμηση δέρματος Έξτρα: Επενδεδυμένο άκρο πάνω μέρους μπότας, Ευέλικτη σόλα, Εκτύπωση ετικέτας Ύψος τακουνιού: Χαμηλό τακούνι 0-3 cm</td>\n",
       "      <td>womens-loafers-and-moccasins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>945376</td>\n",
       "      <td><img width=\"150\" 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       "      <td>12,5 cm ilgio užkandžių šakutės WMF Nuova</td>\n",
       "      <td></td>\n",
       "      <td>kitchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>852217</td>\n",
       "      <td><img width=\"150\" 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\"></td>\n",
       "      <td>Didelė Moteriška Piniginė Liu Jo</td>\n",
       "      <td>Liu Jo Didelė Moteriška Piniginė Ecs Xl Bilford AA2072 E0086 Ruda</td>\n",
       "      <td>womens-wallets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52017</td>\n",
       "      <td><img width=\"150\" src=\"data:image/png;base64,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\"></td>\n",
       "      <td>SENSOR MERINO ACTIVE pánské spodky černá</td>\n",
       "      <td>Vlněné pánské spodky jsou vyrobeny z vysoce kvalitní vlny Merino z Nového Zélandu. Funkční spodky z vlny merino jsou velmi příjemné na těle, a díky jemným a dlouhým vláknům přirozeně absorbují a odvádí vlhkost, hřejí, i když jsou vlhké. Funkční prádlo Merino je odolné proti zápachu i při delším nošení. Merino má vynikající termoregulační vlastnosti .           SENSOR MERINO WOOL from Sensor Activewear on Vimeo .</td>\n",
       "      <td>mens-undergarments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1091966</td>\n",
       "      <td><img width=\"150\" src=\"data:image/png;base64,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\"></td>\n",
       "      <td>MSGM Kids fruit logo graphic T-shirt - White</td>\n",
       "      <td>White cotton fruit logo graphic T-shirt from MSGM Kids featuring a round neck, a graphic print and short sleeves.</td>\n",
       "      <td>girls-t-shirts-and-shirts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300304</td>\n",
       "      <td><img width=\"150\" src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAQDAwMDAgQDAwMEBAQFBgoGBgUFBgwICQcKDgwPDg4MDQ0PERYTDxAVEQ0NExoTFRcYGRkZDxIbHRsYHRYYGRj/2wBDAQQEBAYFBgsGBgsYEA0QGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBj/wAARCAEqAOQDASIAAhEBAxEB/8QAHQABAAIDAQEBAQAAAAAAAAAAAAUGAwQHAgEICf/EAEoQAAEDAwMBBQUEBgQLCQAAAAEAAgMEBREGEiExEyJBUWEHFHGhsSNCgcEVMlJicpE0NdHSJTNTc4KEkqLC4eIIFhdDY2SUo/D/xAAUAQEAAAAAAAAAAAAAAAAAAAAA/8QAFBEBAAAAAAAAAAAAAAAAAAAAAP/aAAwDAQACEQMRAD8A/fyIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAi8ue1jcucAPVac9cWtDom5aD3nHyQfLpfLLY4GzXq70NujecNfVzsiDj6FxGV9or1Z7k3dbrtQ1bfOnnZIPkSvzj/2ndC116FDrilbLV01DAaargbl3Ys3FzZQP2ckh3l3T0X5+tVPA2VpjeIyOm0bUH9HUX480rcrpStaKa8VkYx0bUvHyyupWq/ahfFgXisOee9MSg7ii4++738My+8VQ+MpCiqm51z3EVd+kI8W+8E/mg7jLUQQMLpp442jqXuAwsVNcaCse5tJWQTOb1EbwSuDvrKZzTsqZ5HEYy1pd9VPWuoNsgFQ2WVkoGQXO5ag7Giplm1g4U4bdiXku4e1oBA9QFbKWspayLtKWdkrf3T0+PkgzoiICIiAiIgIiICIiAiIgIiICLXnqeyA2t3E+a031Uz+C7A8m8IJF8scYy9wHotSWuJyIhgeZWkXfFeMoMrnue7c5xcfVN/dwsS+FyD6Wja5gaHsIIdGRnjx48R6Ljeq/YLZK+tkuek6iO1TPO51FI0upyf3ccx/DkegXX3P8QsT5MnnHxQcCpPZnqS1y7aq2wStH/mQzNcD8wfkpSspL5QOobdZbBDNV1Be6Srqw73ama3H+MLDuLnZw1oxnByRhdcqMOYeh+KipYG7iW07D+KCgafg1RLDVs1bZbRRSxzbYZKGpMsU8ef19ru9GcfdOVJy+7xv2QQN+IYB9VYzSyOz9lGB/Fn8lo1NG4uwGN+ICCNjJAwMj0CygDq7nxx4LOKVwGMJ7u4IPBkOOqzUtfVUcwlp5nxuHQtOFjMTgOi8lmEFztmuXACO4w7x07SPg/iPFWuhutvuLM0lSx58W9HD8CuQBuOhWSOWWJwdG9zSOhBwUHZ0XM6LVt3pAAZTOwfdl73z6qz2zWVFVtxWROpndN36zT+aCyovEUsU8QkhkbIw9HNOQvaAiIgIiICIiAsE1RHGdrnYKzE4Ci6wsyQfmg9SP3tIWvyD04WCCYtf2TuQRgEr32mM4P8ANB7OMLyvHacL5vJOEGQHB6r4XDHRYi7lfCchBjkftzhaUszhnC2ZD15WlMeDyg1pKh2MYWp7w4yEeC9ynqsGRkoNyN7fFa1RM0vwAF5DiBhYngk8oPL6hrQTtC1H1jie60Be5Wghajm4JQDI9x7zivm7nqvPoiD3uC+byvBamD4IPe85WSOYszg8+BWuC7djHK9DLnu8EFgst6qKCfc2Xuk95p6H4rotBWwXChZVQOBa75FcgbhjcE5Kvejqn7J0Jdw7oPLCC2oiICIiAiIgxyHDSoS4PPZlw8+SpeZ3cOFAzyjeWnBB4IKDUjqN2Ccbmn+YWUzAEg/NRVVNT22QT1M4jiLgwE9Mk4Wvcrg+CIPLuTwcdPJBPe8xNzlwC8tuED3BrDlVqkbU3CQNj3bfEq1W60xUsQc8bneZQCSR44K+5w1bL4244HK1ZBgFBrSHgrQmf3gOMk4A8/Rbcp4JVb1RarXfNMVtrvPZiilYC+R529mQ4Oa8HwLXAEHzCDbkPVYBxkLmGk9VXGohraBl1jElNJJBE+ueKiI7XbB2u12+E7sDlxHebkN3DNNo/avq69SXCVupdJWamt03YVs9VMRHSTEkCnke5sbe04PdBLuDnog/QYJwsbsuOACT5BcJpvahrKk1habBNe9KXF91IfQysncI6yMnD5YnhrmvZHglwa8EYOcYU/qe/S3O+0Gnqi8PZR17+ymnieKZu0sLssj3B7m7QXbnEnbg7QHAkOoOwW5BBB8RytSRZqOhpLdaKa32+BkNJTwsigjj/VaxrcNA9MALDJgEoMS+OlDQcD5L74eSDGeeiDB77Fu2ucAfXhZQ9rhkEFeKmihqIyCAHeDgoR0tTbpzHINzPAoLAMZXlpDdxJ8VpUtayYcHB8kZNvlIyCg3RIScq2aXqCyUHJ4KprTlwAVjsEu2oAPCDqQIc0OHQ8r6tS2zdtb2nOS0lh/D/wDBbaAiIgL444YSvqxynDMINad32SrF0kMFSJM9yTgnyKsc57pCr1yjFRSywH9YdCggL1HHc7RPQSnLtpLfUeKgdO1U2pLTHaZHD9J0E4p6pp4y3Hcl+DgP5grYqKiZjgcnexypOoTc7bfm6k0/3Kpjds0GcCoizksPqOoKDvVLBa7VTNjNRCzA6ucBlbLamCVmYpWPb5tOVxv2d3Ol1nrJr6p7po6aMzdhKc97OBuHofBdlkpoM4ZE1uOBtGEGJz2noVry9Cs/ZtZ/zWrUPe1pLG73gEtb+0fAIKlf9UxWuplp2U5cadzTUy1OYYY2FhfxIRjcQAADxlw64dj8++1DV+v9R6Ir36Zjt94fSVJiNus9SWyS5c5ofIQ7LI2EDe8EEg4aYyXPbZNZXZtrs0hNNF73NTuFAyVvaCSpfUmIkuyRJsyxm/xD3OGDlUK++zLTrdDQ6keyCa5uIoGk1csc5YHg7ZcObw4h73A9HTOfknAAaum9Q3GxVmkbPT2SjpbpVvJjoaWCLMDHxtp3wu2v7ORj5jK4tyC7buLgWNBuVts9ysftQazT9FbrZT1tZNW1tsuuc0lTK1sXvdK/a4Pik2tY5hwWOLcPAdzTJLNM+gtUelpY7ferBIBS0x7oliY/dGGBwONu1uARgHtGu27w5TTL3qzXutLTZfaGWadtbJXQthpGtY+qkkZsy4Oc8xscTs3O7oLw0ZLgWhK2qyXO6+0F16uVot91uFBUVL7Tb7c3dTUTahrd9XUucIhJLMGNLYsMa1pDiTuBPPb/AKgvmqdFXeqtenKS53iCvlcaWsDWSy1EhY+KUyF7cFjWyt+yLXtADASGtcbTatX+0DTz7jYtDSDUVrlq5JKOvqImGaAud3pHAvG0HG5pc0tcCHN4OwQz9KUrdJSaduEjLrXSze91kYeezj2jcxhdwQQWtO7uuzvfho2oLrovWmqLDpqy0mp56Cikq29sbddpwKyBneJieQQGvDWlweRh+Wna0l66Va72Lk8U00RZUiN0pMQc6B7Q/buY8jkHLT+PUkEDhjfZvabRoCDU1jMBula1hc90z3yvkib3HyNJcNoLXAt6NZM9uS08XPTNzp7nBTXO1U7XsM1I+KMPEWJJGOc9m77nAa8t+8SQByg6lhfF9JAzg5C843chBkDvAArBV0UdZCWPGHeBXvsN7SJCcYwQDhVPUNwn01cKeWnqn9jNndDK7IGPEZ6IPdO51DPVCqfsbB1cTwt+hkxD2pBy/GAfkqLU36XVGpI4qePsqFjmySn/ACzh0/AK60zxnxx4IJdhx1PKl7VJsqmD1ChmHICkKE/bDnlB03T9SHT1NOT0w8fQ/kp1UawVhj1BGXHiQbD+SvKAiIgLBK8bsLOsb4WP5OQfRBFzTAyEYyPioGvlZHUyYcQQFZJ7a9wJie0nydwqzerbcY45JBSyPHmzvfRBT7hK2OpfKMOB8lWL1UTe7ve8DAHAHRbtbNUmrLHNLSDnaRhRl1eTRPD2+Higi/YxcIqL2219OyIubXUhxj7jw4E8euF+k3O6nK/N3sztr6P2g0mo2A4lq5LfjwI7HtCfjkhfotz8tyRtJ8EGOR2Mqqap1O/TxoxHYbrdXVLntb7g1h7Mtbu725zcZAcR/CfTNlmcMFVTUtrt98pIqa4tkfDHKJdjXbQ/AI2u82kHkIPz17UrpXTyRw0mhNTw0omEzaWOkcC0iN3da+IvDTvax4OBtIHgGg06HW2lrDcaZ96ser4KC2R0+6a+MBfCH4aHsBib2kjXNa1oIeXh8hAaeT0X2vaQoLVoQ3Ggq7ixkLtlXIZw6R0T8tmkzj/GOiMjC7xGB15XK/ZZT3616Cttqv8AX1Yr5Z2xSyS0NLNt92bJFDhxcXBrYYHlr8Nf3c5w5pQWHVWmY79ru6RaT1a20T00dPcKagFLFWwyUkriwzNhLg4AS7+QQ9vALcYxtak9m2mP+59fSaq1NU3GmhdDJU1ETzbJJ5zvPYxxRA/c2nMm8gjpySKrqezQ1Gqnn9I1NFca+eKknv8AEezqKbY8ukL5C0NbFJJ2zMMz3ojnAJUjWXC/3C7w02sNQvjki2zU+orSInuigdG9m6pMZcw5bljsNBy1rg12SAE/SezehdpOkpdMayFC+Gg7ehqiw3ds2HOEjHNkxI3a4tb3XAcE7RyA0fbbPo28179X6pN4dbLSLncg2mijFC6UEwBlPGXYcWslLWDc4ktc77oEVZqi8VlLd7PbdXusNA1j23G5XDs2XGsdM92DG97gxjXEAksB2MYA3vOIbpaCt8MN1jr4K6rpbmYPc33R7R21U6HOZpSG7X9pH7xtyQ7bC7jJyg1qrVFnvFvraez6a1i6Gqp219LHZHZgfuc3s8vbE7AcGNkcWkDc6RgaMkm5ezq5VtvoA2p0ZqGohpnyugj9x7Hb9qS157Rze81rWjpx+DSuee1Sju95strp6esroqOmrBRXE01FS0u2jZOZp24jcC1rZaZxaQ0d5oye+3PY9F6Np7joagrtRzVVTXVBFTLiUBpJOXggDkPcXb/2uOiC82S9G9W6SqdbK23mOZ0JhrGtD8t4J7pIxnj8FKs6KKtdvgtVtbQ0z5nxtc526Z+95JOeThSURyg2QeFzH2jwit1XaIJTiFscry39sgDj5hdMz3crnHtAhe/UtmmYOGtlBPxxygj7JTthlJ28k5OArjSOG/b4eAJVPpnGOQPc4AZ+8cK22ykrKsgW+graw/8At6d7x/MDHzQTMXA+C3Kd7muBA5C3bfo7VlUxp/RDKQH71ZUNb/us3FWSg9nVUMOuN4Y3zjpYf+J5P0QRNuqnNq2nHQgjHguo08rZ6WOZpyHtBUJSaNsVNy+CSqdnO6okLvlwPkp1jGRRiONjWMaMBrRgBB6REQEREBERBgqKKjq24qqWGceUjA76qEq9C6UrmkT2eLn/ACbnM+hViRBz65aQtGnY7NBZKUwQsuT6hzS9z8udEWk5JJ6NH8lPzlwiDyR6Lcv8YfR07sfqTtPyIWrIxrou8eg6IIatq3MiO0ZKrVXWy87gVbainaIHOIVYroQXEgIKnqKaCbTVfHWNLoTA4njPI5bgeecLilso7PpWimuLwz3ejgA2SMaC0YLzHuxudu2hveLj9oBnAAHUPaa2QaArKWljklqJ3NDIopuxe8NcHvAd4Ha3HxcFzR+k6vWEM81sjpjWiaHtnPqHN90DI9z42vbns5HSOBIbgt7FocT0IV2zz6ktlxlhqLg2oqD9rPJXB8PZuLntd2ex2Q0CYOLuOpPTJM5VxVj78NQWyH9KSVFRBR1D6CjbTxvqiC11O1m89oAA4PdnGTy4kcVi+0N105fYrdqGsLKindHPHPAMBrT2X3mt24c33sO4aHBj+43gL5Y9TTU9JSvuFZBbZIHl1NEG9mIgGOYx2+V0bs7Xuc0N3Bu7qQgs+nTJFdJ75LG2z1FZTEUctbRCaKVnaPMrWS729lJHteSM7tvg4NKg57hfBqhl3krHN9zkbXTUtNG+Rk0eZJpWybjkO2yFuccNeQcHrguGrHSMigjq4Ki3GtdUVA7MbIQZDJ2gMRlDmte5xAe4YMhPhhfdJ2a+alabfZq+cPp4RJVSTSua55d2e5xlcDiQy+8AvAccscBtw1wC03a0Wi41fvXaCeB/ZVLTCxv2xjfgSE4ycjDsE4O/vA4GOxxTU7KZkdMQIWtAjx+zjj5YXDqmwT6Stvuk9HB+kZKOSE0tHM5jaoB4LXx9NzxE5zCeXjsmuJdjnp2me2fo63RzuPbwwMgly/ed7BtPe8c4Bz5EILM2oac4K2IJhnqomKJ54DlvQRSNlDXceqCYhBf91zh6KwWHRdg1EZ5L5b21Qi2iLL3N2ZznBaR5BQtFG9rsCTLfLC6LpKLs6Gc+JLfoUGa3aP0vagPcLDQQuHIf2Ic7/aOT81NAADAHC+ogIiICIiAiIgIiICIiAiIg0Lu3dQN9JGlR2MtwpW4jNCf4h9VGNblyDTuOGUwCrVa0hmSrBdHZk2D0Chrk3bToOaass14u17oo6VlG637Hxz9sTuYTk7sfeGWs48SPAZKlqWiprfQxUVHCyGnhYGMjYAAABgdPgFJS8vJz4rWIQc99pckNZam6chiifV17XNkcXYdFAWSMe8gd4gNfIQMjkY+9g0Gn09QWqWO3UdvbDRxwNp4Y4+2f2expHAjcMkMaXEnr2Q8XLpuqYKxuoqWoh7R7ZIxCxsdJkNfvAZvlJ4BkdG4jrtiI5yVBUlspK67UsBpIZqSWWMsMkeRt3xYGD5xyfX1QUKr0zQ6jt9wtl1pIm0cmYg1/auMT2PLnHa85DmSNDODyIR+0ur6IuLa6xyQVNPDT3KmeW1cMb95aXPe7r1xkvxnPnk7sqt0tO1ssTKVphmAYxrHHuMeWx7TjoRvkYSfj4qwaObK41tZtmjp5dgiiqKbs3hoblnfBIeA0iM+sYOckoJu8WqlvNont9XHG9sg7pkjD9jhyHYPkQPj0UPpqgu1vbXRXKKliidPup2QOLsN6ZJ9QGY6HjkA9bQR3CtZ4xkoM0DMjKlqeFs0GejmqJpHd7GFN28bJseBQbtBGd2CchdE02NtFKPUfRUSmaGyt8ycK+ad/o0w/eH0QTKIiAiIgIiICIiAiIgIiICIiDWr/AOgu+I+qi4/1zlStaM0Mn4fVQ4O2N7vIIIup+1rz6FRd8O2ENUrCC+qe/wAAoS+yDeGoIF2d3B9VjcOD8FmaMtLvTC8OAJIyPBBD3u0w3izS0U7WEkExGTJa2Ta5rXFoI3Abuh4KpTLVX2/UdFFdGl1JHWxStq43bIwA9+wPPAaTshAYOAMZccrozxg59Vp1MUU8EkM0bJY3Da9j2hzXDyIPBCDmkNqvd8EsDQylpHdoO2mjD2TMc6YNcG9HtIbCduWubgHJBV8oaemoaZtLSs7KBhJazcSG5JJ65PUle3HBIGABwMLyx3eQbjv1SsQAexwPVe3H7M/BfIDnIQYKc9nUbc9FYaVpMbZAq/M3ZNuCn7U8vp9pQTFI7dVRjCvOnDmnn/iCo9EM1W4jorvpn+izn94fRBOIiICIiAiIgIiICIiAiIgIiINet/oEvw/NQcrsUbj58Kbrv6ul+H5qv1LsUzW+ZQYqcYhe/Hgqlepd9UWjw+itzz2Ntc7PUKkVrjLVkDzQeWNHYOOPABYR3nYHmtuRuyhcfMgLUjPhnnxQYJv1wAtObgOx5rdmGHcrRm8R6oNN3XHkvGe94L079ZeCOcoNlrssxwkDgJDhYozweF9jO2ZBsVLcxZwt61P2nHotZ7d0GEonFknxQWugONzleNM80Ep/eH0VGt/9Fe5XnS4/wS8+b/yQTaIiAiIgIiICIiAiIgIiICIiDWuH9Wy/AfVV2fLnsYFYrh/Vs3w/NV9ozUE+XCDUvEoht+3IHCp0Te1nfJ4ZVi1HLloiHJJURTwhsZwPEIMFcezpQ1adL3uT5LJdZcnYPBeKXDWDCDFV8OCjql3Qea3647SVGTnhpQa7zgn6rxnPivTzyeV58UH1riCRlet53glYyBnITPJ4ygloTup/Ary0YlHxXmidmIhZXNw/ogslsJNC7kK/6ZGLH/pn8lzy0PzRvB8F0XTYxYm+ryglkREBERAREQEREBERAREQEREGvXDNum/hKr8XG53hlWKrANBMD+wVW5HCKjc7xwgrl0cZrgfEN4/FYXnsaYDGCeVnkYDLucOeq0K2TuuGfRBEVTy6Rxz4cLPTAiNufJar8GQlbcBy35INSuOXHGcZUZMe6OFLVfDHEqIlII8EGvnvEo45GQh818+6EDqF4PQ/ResgFeCepQbtHIWux0Un+u0HChYH4cpqneHbfVBL2ggB7c+C6Xp4YsMfqXH5rmNv7lZjzXUbCMWCD4H6lBIoiICIiAiIgIiICIiAiIgIiIMNWcUE38BVWqzmHZ5qz1xxbZz+6QqrUHI6oIipP2p54aFB1T9wIJ8cqVrn7d+D+KgpTlp5zyg13EDPmT0W3BgRcnxWmBl5OeAtpjgGZQa1c7hwHkoh5JHw8lJVbtxd9VFSHh3CDGSM4yvrehyvGcOxhGOHOEHojqsZ69F63HOeF5z4oPcZ5UrRvGWgeaiW9c4W9TOLXoLPSj7dsg8wuo2QYsNMP3fzK5ZbjvHX1XVbRxY6b+AIN1ERAREQEREBERAREQEREBERBqXI4tc3wH1VUndwc9FabqcWqT4j6qpVJ7hQQNe/9booeRwAIA+SkK8u3nJUYRnk8oPLTj+S9F+G9T/JYiecLySSDlBiqHZz/Yo2TODjhb8hHOcD4KPme3qCUGIu6rw1wyvL3+HP814a/k8oNgHjPROowsTTx4r1k44KDJGfBbcB735rSYcFblO4F/4ILLand9oXW7T/AFLTf5sLj9rdiRvxXX7Qc2Ol/wA2EG6iIgIiICIiAiIgIiICIiAiIgj7ycWpw83AfNVGqdtYeVa74cW5o85B9CqlWENjOfwQVuuk+2OfD1UW+fB6tK3Li8mV2CeVrwUhI3vOB6hBhYXOJ5yvTmnGN/yWziNgOH5/Bas8kbCQCMjyCDRqQWsdl+B8eqhqiVzScElbdU8vkdnGFHSNBcT4oMQnkJWVjhk5A5WEN6rIweACDYaW7eq9g8LE0nB4KyNKD7lbMDuhWuMHwwskHB4OUFntbsvbzldisZ3afpT+5+a4zaT32rsOnHbtN0/PTI/3iglEREBERAREQEREBERAREQEREERfT9hEzzcT8v+aqlfvEZ4yArNqAPLoSAcAHkeaqldLO0YGw/xcIKtU7/ei4DOFqzVVQe6AWj+a3qm4TMyHQDr91//ACWmKxkgJkg/+wIMETJnuJL3Y6lYq4djGS54BP4re97oohy3Bx+2FDV01JUVLtwkwMAbS0oI1xc95w8HnyXjsX4zkn4KRY23NIaIJSMdTt5WQm2A/wBDnI894CCJDcfdJ+C+jIzkY+KkXVNE3Oykkx/GFj96o/GBw/0wg1GnDsOIHosjfwCze+0fOIXkfxBfBU0vUU7/APaH9iDwG56FZI2nOfH0TdTvyewyfVyNOx+WRNb8ckoLFaRy3xXW9KOzp8NznbI4fn+a43bXyPLcu2j0GF13RrJG2R73NIjc/LCfHjBKCxIiICIiAiIgIiICIiAiIgIiIPMkccsZjkY17T1DhkKHrNL2ysB5mhz/AJN3HzyppEFKl9nNE8nZc6poPgWtP5LEfZnSEcXST8Yh/ar0iDncvssjeT/hNpz/AOiR/wAS0JPZBI95IvEIHgOwP95dTRBytvsgmaRi9wj/AFc/3lnb7KJA3BvEPx93P95dNRBy93siL3Z/TbAPSnP95Ynexsk92/NH+rf9S6qiDlX/AIN5H9et/wDjf9S+x+xkD9fULh/BTf2uXVEQcxb7Hadp51BOfhTtH5rdpvZNa4se8Xaul9GhjPyK6CiCv27RenraWuiojM9vR07i/wCXT5KfADWhoAAHAAX1EBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERB//Z\"></td>\n",
       "      <td>Steampunk sova čiernobiela - Klasické pánske tričko - XS  Biela</td>\n",
       "      <td></td>\n",
       "      <td>mens-t-shirts-and-tank-tops</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>908951</td>\n",
       "      <td><img width=\"150\" src=\"data:image/png;base64,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\"></td>\n",
       "      <td>Jordan 23 Engineered Parka striukė</td>\n",
       "      <td>Sudėtis:   46% medvilnė    38% poliesteris    16% nailonas</td>\n",
       "      <td>mens-parkas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Multilingual CLIP Model\n",
    "\n",
    "Okay, we loaded the data set. We saw the various items. We saw multiple languages, saw multiple categories.\n",
    "\n",
    "Now we will load the model that I described above. The model which is made of two parts. One part can encode text. regardless of the language. Another part can encode the image.\n",
    "\n",
    "Because the category itself has a name, we can use the name to later encode the category itself into the vector space as well. And then we will work on the vector space, find similarities between products and categorize the items.\n",
    "\n",
    "Here we use a model called CLIP. It is a model from OpenAI trained on datasets of images and their captions from the internet.\n",
    "\n",
    "And we use an open source textual model called Multilingual CLIP for encoding of the text which was also trained on millions to billions of images with their captions from the internet. In our case, this is an open source model and it was trained on various languages such that it map all of them to the right images. It doesn't mind seeing Cyrillic text, it doesn't mind seeing Greek letters, in general it doesn't mind seeing different language texts. It can still encode them correctly close to the images they correspond to.\n",
    "\n",
    "Images are naturally multilingual. It may not be 100% true everywhere and all the time, but mostly it does not matter which country we are located in which language. The images generally for the same categories look similar enough such that you can, in most cases, work with models spanning multiple languages. It really depends on the use case. But in general, we can say that the images are already translated. They are already language agnostic.\n",
    "\n"
   ],
   "metadata": {
    "id": "C2wuvZ7m3Nes"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import clip\n",
    "from multilingual_clip import pt_multilingual_clip\n",
    "from multilingual_clip.pt_multilingual_clip import MultilingualCLIP\n",
    "import transformers\n",
    "\n",
    "model, preprocess = clip.load(\"ViT-B/32\", device=device)\n",
    "\n",
    "text_model_name = 'M-CLIP/XLM-Roberta-Large-Vit-B-32'\n",
    "text_model: MultilingualCLIP = pt_multilingual_clip.MultilingualCLIP.from_pretrained(text_model_name, device=device)\n",
    "tokenizer = transformers.AutoTokenizer.from_pretrained(text_model_name)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342,
     "referenced_widgets": [
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    "id": "xTCvXpeIKEzI",
    "outputId": "3d9573c5-2f1e-4a88-d53c-7efb4e58690a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|████████████████████████████████████████| 338M/338M [00:03<00:00, 112MiB/s]\n"
     ]
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d475af7c35744c8cb9e6fdc8f5748197"
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     "metadata": {}
    },
    {
     "output_type": "display_data",
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       "version_major": 2,
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     "output_type": "display_data",
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    {
     "output_type": "stream",
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     "text": [
      "Some weights of the model checkpoint at xlm-roberta-large were not used when initializing XLMRobertaModel: ['lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.weight']\n",
      "- This IS expected if you are initializing XLMRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing XLMRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
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   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We downloaded the models. Those were both together around four gigabytes of data. Now let's look at how many parameters, how many numbers these models are composed of. You remember where I mentioned that these models can turn bytes of image data or bytes of text data into a single image. into points in vector space. Now, this is the function. This is the code that does it. It actually contains lots of numbers, which are then used to multiply or sum up with the bytes of the data of the image or text. in multiple steps, we do this in multiple layers, these kinds of operations. And we tune these numbers in these models in such a way that we can reproduce this, what human would expect, the similarity, that similar things are similar close together in this high dimensional space. Now, These models are not a small thing. You saw it's four gigabytes of data. And if we have a look at the cell below, we can see, we can calculate easily that they both have around hundreds of millions of these numbers that are used to apply on the bytes of the input data.\n"
   ],
   "metadata": {
    "id": "6GuiOYSwy7Ne"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "model.eval()\n",
    "print(\"Model parameters:\", f\"{np.sum([int(np.prod(p.shape)) for p in model.parameters()]):,}\")\n",
    "\n",
    "text_model.eval()\n",
    "print(\"Text model parameters:\", f\"{np.sum([int(np.prod(p.shape)) for p in text_model.parameters()]):,}\")\n"
   ],
   "metadata": {
    "id": "EWiq9HP_rEii",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "4d08a1d3-8ca7-4533-f305-3cda6e4cc158"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model parameters: 151,277,313\n",
      "Text model parameters: 560,415,232\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "JAuDHTPb6w93"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Create Embeddings (Vector) Representation For The Above Samples\n",
    "\n",
    "Okay, now we have the models loaded. Let's take our 10 samples and let's turn them into those points in the high dimensional vector space. We will do this by iterating over the samples. Then we will concatenate for each product, its name and description into a text. We will then open the image and convert it to format that is suitable for calculation of the model, which will be performed on our graphical processing unit GPU. After that, we then execute, apply, we apply the models on those bytes corresponding to the text and corresponding to the image. And we get the output vectors, the arrays of numbers, the points in the space that I told you about. We do some additional operation to help us then do this comparison for the similarity purposes. And then we store those representations and also print them below. Below you can see the points in the space as Yayi mentioned, those are, you know, numbers. These are arrays of numbers somewhere around zero. They can be positive or negative.\n"
   ],
   "metadata": {
    "id": "Fam6Un5x3kI1"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "import skimage\n",
    "import IPython.display\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "from collections import OrderedDict\n",
    "import torch\n",
    "\n",
    "\n",
    "image_embeddings = []\n",
    "text_embeddings = []\n",
    "images = []\n",
    "texts = []\n",
    "\n",
    "for row in sample_df.itertuples(index=True):\n",
    "    # print(row)\n",
    "\n",
    "    text = row.name + ' ' + row.description    \n",
    "    # tokens = tokenizer(name)\n",
    "    texts.append(text[:30])\n",
    "\n",
    "    image = Image.open(row.image_file).convert(\"RGB\")\n",
    "    image_input = torch.tensor(np.stack([preprocess(image)])).cuda()\n",
    "    images.append(image)\n",
    "\n",
    "    with torch.no_grad():\n",
    "      image_embedding = model.encode_image(image_input).float()\n",
    "      text_embedding = text_model.forward([text], tokenizer).float()\n",
    "      # text_features = text_model.encode_text(tokens).float()\n",
    "\n",
    "    image_embedding /= image_embedding.norm(dim=-1, keepdim=True)\n",
    "    text_embedding /= text_embedding.norm(dim=-1, keepdim=True)\n",
    "\n",
    "    image_embeddings.append(image_embedding.cpu().numpy())\n",
    "    text_embeddings.append(text_embedding.cpu().numpy())\n",
    "\n",
    "    print(image_embedding.shape)\n",
    "    print(text_embedding.shape)\n",
    "\n",
    "    print(image_embedding[0, 0:8])\n",
    "    print(text_embedding[0, 0:8])\n",
    "\n"
   ],
   "metadata": {
    "id": "tgkslIPPNY-0",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "49be483a-a0f8-4bf5-9f54-529726d0a252"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0512,  0.0167,  0.0421,  0.0127, -0.0400,  0.0267, -0.0176,  0.0003],\n",
      "       device='cuda:0')\n",
      "tensor([-0.0168, -0.0202,  0.0162, -0.0023, -0.0431,  0.0337,  0.0169,  0.0172])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0001,  0.0117,  0.0252, -0.0022,  0.0160, -0.0138, -0.0799, -0.0268],\n",
      "       device='cuda:0')\n",
      "tensor([-0.0108,  0.0487,  0.0182, -0.0063,  0.0064, -0.0180, -0.0807, -0.0243])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([ 0.0221,  0.0322, -0.0228, -0.0022, -0.0297,  0.0031, -0.0550,  0.0307],\n",
      "       device='cuda:0')\n",
      "tensor([ 0.0173,  0.0583, -0.0090, -0.0133, -0.0730,  0.0263, -0.0294,  0.0269])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0045, -0.0321,  0.0263, -0.0080, -0.0238,  0.0166, -0.0204, -0.0195],\n",
      "       device='cuda:0')\n",
      "tensor([ 0.0016, -0.0252,  0.0276, -0.0092, -0.0415, -0.0032,  0.0008,  0.0128])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0226, -0.0084,  0.0166, -0.0125, -0.0146,  0.0344, -0.0055,  0.0530],\n",
      "       device='cuda:0')\n",
      "tensor([-3.3906e-02, -3.5226e-02, -5.1603e-02,  7.6702e-05, -3.8097e-02,\n",
      "         5.6928e-02,  2.6011e-02,  8.6248e-03])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0068,  0.0186, -0.0258,  0.0790, -0.0233,  0.0064, -0.0422, -0.0364],\n",
      "       device='cuda:0')\n",
      "tensor([ 0.0064, -0.0235, -0.0302,  0.0320, -0.0213,  0.0147, -0.0489,  0.0709])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0036,  0.0128,  0.0098,  0.0299, -0.0493,  0.0331, -0.0578, -0.0778],\n",
      "       device='cuda:0')\n",
      "tensor([ 0.0241,  0.0500, -0.0251,  0.0281, -0.0666,  0.0459, -0.0274, -0.0155])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0203,  0.0433, -0.0124,  0.0129, -0.0166,  0.0232, -0.0122,  0.0017],\n",
      "       device='cuda:0')\n",
      "tensor([ 0.0420, -0.0070, -0.0363,  0.0437, -0.0623,  0.0257, -0.0069, -0.0169])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0046,  0.0403, -0.0009, -0.0151, -0.0278,  0.0227, -0.0090,  0.0392],\n",
      "       device='cuda:0')\n",
      "tensor([ 0.0218,  0.0260, -0.0437,  0.0475, -0.0580,  0.0675,  0.0097,  0.0648])\n",
      "torch.Size([1, 512])\n",
      "torch.Size([1, 512])\n",
      "tensor([-0.0008,  0.0136,  0.0031, -0.0129,  0.0046, -0.0060, -0.0478,  0.0357],\n",
      "       device='cuda:0')\n",
      "tensor([ 0.0234,  0.0457,  0.0014, -0.0125,  0.0569, -0.0429, -0.0663,  0.0310])\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We have those vectors now, we have those arrays of numbers, those points in the space. And now let's do the comparison. So for each image, we have a representation for the text and we have a representation for the image. Now, if you'd like to do comparison for each text with each image, and see if we will see any kind of pattern that the corresponding text approximately matches the corresponding image. Let's see if this works.\n"
   ],
   "metadata": {
    "id": "KXClSJTE0Tm4"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "similarity = np.zeros((len(images), len(images)), dtype=float)\n",
    "for i, image_embedding in enumerate(image_embeddings):\n",
    "  for j, text_embedding in enumerate(text_embeddings):\n",
    "    dot = image_embedding @ text_embedding.T\n",
    "    similarity[i, j] = dot\n",
    "\n",
    "similarity"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9sLrWsRGAMdZ",
    "outputId": "088910b6-a918-4257-9354-9160a2e61d9d"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.32170546, 0.1596625 , 0.21481672, 0.23024017, 0.15850498,\n",
       "        0.17439416, 0.20271295, 0.19091384, 0.06432018, 0.19397044],\n",
       "       [0.19960701, 0.30962235, 0.21893699, 0.17018083, 0.12392934,\n",
       "        0.19059448, 0.22055674, 0.12949976, 0.11513549, 0.28352517],\n",
       "       [0.21880822, 0.27415538, 0.35283637, 0.19493261, 0.20521465,\n",
       "        0.244425  , 0.30580205, 0.1649102 , 0.13952287, 0.25590718],\n",
       "       [0.24838777, 0.19186252, 0.22160567, 0.30410847, 0.15062648,\n",
       "        0.18293893, 0.23480579, 0.10434719, 0.02291686, 0.16708688],\n",
       "       [0.20428148, 0.17761719, 0.24677274, 0.18205445, 0.33977458,\n",
       "        0.16829696, 0.24325666, 0.16073345, 0.12827782, 0.15227771],\n",
       "       [0.18925637, 0.20889492, 0.20430066, 0.21231253, 0.15327214,\n",
       "        0.32999116, 0.22152679, 0.08503684, 0.04969195, 0.11073164],\n",
       "       [0.18766129, 0.23361586, 0.34878442, 0.19290467, 0.16950344,\n",
       "        0.16018705, 0.355151  , 0.12505883, 0.13422872, 0.18793392],\n",
       "       [0.2465048 , 0.19663101, 0.19250725, 0.16994172, 0.16185777,\n",
       "        0.19222407, 0.21397319, 0.32922924, 0.2130594 , 0.23858227],\n",
       "       [0.2295907 , 0.19842236, 0.18768799, 0.16638654, 0.16458049,\n",
       "        0.16412742, 0.20996135, 0.30654058, 0.34776598, 0.20468786],\n",
       "       [0.20246702, 0.28726366, 0.30746502, 0.16052723, 0.16094434,\n",
       "        0.15787721, 0.26819801, 0.11877243, 0.10245426, 0.3199296 ]])"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Okay, I have printed a path to matrix, which is a result. So for each image, each text, so we get a matrix. We get a matrix of values where if the value is close to one, it means that it's perfect similarity. If it's close to minus one, it's high dissimilarity. And now we would like to display that.\n",
    "\n",
    "Note that some of the code is adopted from [Open CLIP Colab](https://colab.research.google.com/github/mlfoundations/open_clip/blob/master/docs/Interacting_with_open_clip.ipynb#scrollTo=uLFS29hnhlY4)."
   ],
   "metadata": {
    "id": "XcYxi8kN0iFb"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "count = len(texts)\n",
    "\n",
    "plt.figure(figsize=(20, 14))\n",
    "plt.imshow(similarity, vmin=0.1, vmax=0.3)\n",
    "# plt.colorbar()\n",
    "plt.yticks(range(count), texts, fontsize=18)\n",
    "plt.xticks([])\n",
    "for i, image in enumerate(images):\n",
    "    plt.imshow(image, extent=(i - 0.5, i + 0.5, -1.6, -0.6), origin=\"lower\")\n",
    "\n",
    "for x in range(similarity.shape[1]):\n",
    "    for y in range(similarity.shape[0]):\n",
    "        plt.text(x, y, f\"{similarity[y, x]:.2f}\", ha=\"center\", va=\"center\", size=12)\n",
    "\n",
    "for side in [\"left\", \"top\", \"right\", \"bottom\"]:\n",
    "  plt.gca().spines[side].set_visible(False)\n",
    "\n",
    "plt.xlim([-0.5, count - 0.5])\n",
    "plt.ylim([count + 0.5, -2])\n",
    "\n",
    "plt.title(\"Cosine similarity between text and image embeddings\", size=20)"
   ],
   "metadata": {
    "id": "fm1aiPRMgx_9",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 829
    },
    "outputId": "03043e05-d590-4964-d68b-8b8e77f381e8"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cosine similarity between text and image embeddings')"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Okay, above we can see that the images approximately correspond to the textual description. Yes, we can see some imperfections, but if we look along the diagonal, we can see that the sneaker corresponds to the sneaker description, that the pants corresponds to the pants description in Czech language, and that the fork corresponds to the fork description in English. I think that's Romanian and then as well the undergarments correspond to oh here okay have a look at this so here we can see that the undergarment does also correspond to pants as well which is expected and that this works but still the highest correspondence is between the arctic garment and the under garment description in the Czech language here. Okay, so and as well we can see that the shirts correspond to each other description as well, which is expected in this case as well. And also the jackets are very close together. So the jacket descriptions are close to the image descriptions.\n",
    "\n",
    "I promised that we will also be doing categorization. As I mentioned, each category has a name. The name of the category itself is a text. We should be able to take this text and also create the vector for it and then use that for categorization because if we get this image and text, together to represent point in space, then this point in space should be close to the representation of the category. Because as I mentioned, all this should correspond close to each other. And we will see if this works in this case. How we can actually do this? How to encode correctly this? Well, so we will take the name of the category. and we will wrap it into surrounding text. And we will do this by simply saying, instead of a category name, I will say a photo of the category name, for example, men's sneakers, a type of a fashion product. And with this, we will sort of instruct the model. We will sort of add the meaning into this little bit, hoping that we improve the categorization.\n"
   ],
   "metadata": {
    "id": "uoa67RO-0ydq"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "category_name_to_prompt = dict()\n",
    "category_name_to_embedding = dict()\n",
    "# category_names = df['category_name'].unique()\n",
    "category_names = sample_df['category_name'].unique()\n",
    "for cat_i, category_name in enumerate(category_names):\n",
    "    human_readable_category_name = (category_name.strip()\n",
    "                                    .replace('women-s', \"women's\").replace('womens', \"women's\")\n",
    "                                    .replace('men-s', \"men's\").replace('mens', \"men's\").replace('-', ' ')\n",
    "                                    .replace(' and ', ' or '))\n",
    "    prompt = (\"A photo of a \" + human_readable_category_name + \", a type of fashion product\")\n",
    "    print(prompt)\n",
    "    category_name_to_prompt[category_name] = prompt\n",
    "\n",
    "    with torch.no_grad():\n",
    "      prompt_embedding = text_model.forward([prompt], tokenizer).float()\n",
    "      prompt_embedding /= prompt_embedding.norm(dim=-1, keepdim=True)\n",
    "      category_name_to_embedding[category_name] = prompt_embedding.cpu().numpy()\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lukouq6qBKMe",
    "outputId": "b736e59c-3bd9-4169-b338-334b01e71058"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "A photo of a men's sneakers, a type of fashion product\n",
      "A photo of a women's bombers, a type of fashion product\n",
      "A photo of a men's pants, a type of fashion product\n",
      "A photo of a women's loafers or moccasins, a type of fashion product\n",
      "A photo of a kitchen, a type of fashion product\n",
      "A photo of a women's wallets, a type of fashion product\n",
      "A photo of a men's undergarments, a type of fashion product\n",
      "A photo of a girls t shirts or shirts, a type of fashion product\n",
      "A photo of a men's t shirts or tank tops, a type of fashion product\n",
      "A photo of a men's parkas, a type of fashion product\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Okay, so in above we created representations for the categories. Now below we'll compare those representations with the product representation. Product representation here will be an average of the image representation of the product and text representation of the product. So we will get single point for the product and single point for each category.\n",
    "\n",
    "We will compare categories and products and we will display that in the similar way as previously and we will inspect if we can see some good performance from this approach."
   ],
   "metadata": {
    "id": "QL9XZ_IO15kW"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "category_similarity = np.zeros((len(category_names), len(images)), dtype=float)\n",
    "for i, category_name in enumerate(category_names):\n",
    "  category_embedding = category_name_to_embedding[category_name]\n",
    "  for j in range(len(sample_df)):\n",
    "    item_embedding = (image_embeddings[j] + text_embeddings[j]) / 2\n",
    "    dot = item_embedding @ category_embedding.T\n",
    "    category_similarity[i, j] = dot\n",
    "\n",
    "print(category_similarity.shape)\n",
    "category_similarity"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JRXoZZfvVLhn",
    "outputId": "e7708c68-a491-4583-d899-cf65f9333c5d"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(10, 10)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.51852506, 0.41766998, 0.46697199, 0.52243316, 0.4180094 ,\n",
       "        0.36533812, 0.46045864, 0.32509136, 0.23381993, 0.40455255],\n",
       "       [0.42012942, 0.53304005, 0.48567832, 0.420984  , 0.39333135,\n",
       "        0.37644657, 0.46456704, 0.34381872, 0.25259992, 0.46431068],\n",
       "       [0.43012574, 0.43227792, 0.56062716, 0.44162464, 0.4272536 ,\n",
       "        0.37517861, 0.55406576, 0.34854493, 0.26555848, 0.40415534],\n",
       "       [0.42664433, 0.3804349 , 0.43028063, 0.530604  , 0.40429512,\n",
       "        0.3945967 , 0.42456287, 0.27093503, 0.19520693, 0.33602139],\n",
       "       [0.40404063, 0.38073593, 0.3978816 , 0.38226137, 0.42889005,\n",
       "        0.32963023, 0.40193376, 0.31640148, 0.19486378, 0.34015796],\n",
       "       [0.3768937 , 0.38942516, 0.41796792, 0.42259246, 0.41655275,\n",
       "        0.51029092, 0.41292846, 0.27225617, 0.18727055, 0.31538275],\n",
       "       [0.42836344, 0.43209782, 0.50132197, 0.42933673, 0.43362936,\n",
       "        0.37271017, 0.519611  , 0.34909192, 0.26129249, 0.3998372 ],\n",
       "       [0.44804537, 0.43074569, 0.44272435, 0.41182721, 0.42313007,\n",
       "        0.36342341, 0.45369425, 0.48180982, 0.33797574, 0.38428631],\n",
       "       [0.42079461, 0.40186477, 0.43998504, 0.39414763, 0.41134414,\n",
       "        0.33313167, 0.46062976, 0.46364114, 0.35780695, 0.38362664],\n",
       "       [0.38168001, 0.52618396, 0.47181427, 0.38200268, 0.38475809,\n",
       "        0.3081398 , 0.42202803, 0.28804719, 0.22414717, 0.53909099]])"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "count = len(category_names)\n",
    "\n",
    "plt.figure(figsize=(20, 14))\n",
    "plt.imshow(category_similarity, vmin=category_similarity.min(), vmax=category_similarity.max())\n",
    "# plt.colorbar()\n",
    "plt.yticks(range(count), category_names, fontsize=18)\n",
    "plt.xticks([])\n",
    "\n",
    "\n",
    "for i, image in enumerate(images):\n",
    "    plt.imshow(image, extent=(i - 0.5, i + 0.5, -1.6, -0.6), origin=\"lower\")\n",
    "\n",
    "for x in range(category_similarity.shape[1]):\n",
    "    for y in range(category_similarity.shape[0]):\n",
    "        plt.text(x, y, f\"{category_similarity[y, x]:.2f}\", ha=\"center\", va=\"center\", size=12)\n",
    "\n",
    "for side in [\"left\", \"top\", \"right\", \"bottom\"]:\n",
    "  plt.gca().spines[side].set_visible(False)\n",
    "\n",
    "plt.xlim([-0.5, count - 0.5])\n",
    "plt.ylim([count + 0.5, -2])\n",
    "\n",
    "plt.title(\"Cosine similarity between category prompt and image + text embeddings\", size=20)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 829
    },
    "id": "qb849udFUGPL",
    "outputId": "f4902968-3992-4baf-9e53-a2fc0007caef"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cosine similarity between category prompt and image + text embeddings')"
      ]
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can see actually in the comparison above that approximately each product goes into the right category. It is not always such a clear decision. For example, here in case of the jackets, the bomber jacket is not quite decisive for the model whether it's a jacket or a sweater. should be in Parkas or whether it should be in the bombers. So this is a difficult case for this type of model. But for example, in the case of sneakers, those definitely belong into the sneakers here. But for example, the loafers and moccassines are actually not so decided. It's very almost classified as sneakers but the Mocassians still are winning in this case. So, okay, nice. And the kitchen fork, it doesn't seem to be very decisive, but it's, am I seeing it right? It's like saying that the forks should be pants. Okay. So we can see that the model can fail as well here. And then yes, we can look at the check its shirts. Approximately what you would expect.\n"
   ],
   "metadata": {
    "id": "godh0hb_2HiV"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Classification Accuracy\n",
    "\n",
    "The classification accuracy here is not good enough for production system. However, it is amazing that without training this model at all, by just having it try to perform the same thing it sees on the internet, the similarity between the image and text, which come together on websites, You can see that we can use this to do at least some basic categorization.\n",
    "\n",
    "See [classification/eval_clip.py](https://github.com/glami/glami-1m/blob/main/classification/eval_clip.py) and [Supplementary Material](https://docs.google.com/viewer?url=https://github.com/glami/glami-1m/raw/main/media/GLAMI-1M%20Supplementary.pdf) you can find results for 32 token embeddings of classification Top-1 accuracy of 32% and Top-5 of 74%."
   ],
   "metadata": {
    "id": "dZGZbwzq-OGH"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# release memory\n",
    "del model\n",
    "del text_model"
   ],
   "metadata": {
    "id": "6WKgy97S2iEL"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Generate Visual Embeddings For The Entire Test Set\n",
    "Now we would like to also try out searching. So since we have this number representation, which corresponds to the meaning, maybe if I have a meaning of an image, I can use that meaning to search for other images which correspond to this image, to this product. And we can do this.\n",
    "\n",
    "However, there's an additional challenge here. We have a huge catalog of items. So there is so many products in the catalog, but you need to make a really fast search. How would you do this? How would you do this fast search? This is something very similar to other concepts in normal programming, where in here, you can use, we work with vectors, but we can use approaches which are similar to a hash map.\n",
    "\n",
    "Hash map, instead of comparing your image to the entire huge catalog, it is able to do quick comparison with something intermediary and then do only a comparison on handful of smaller amount of products. We do this by sort of bucketing. So we quickly decide into which bucket we should look. And only after that, we look into the bucket and only compare there. And we can save time here. And you can do this hierarchically. And you can find things much faster. This is also called approximate nearest neighbor search. We will do this here.\n",
    "\n",
    "We will use this index which is called ScaNN and it is from Google. It's implementation of one these the indexing approaches, one of the libraries you can use for this. And I will show you that you can do very fast search, which is kind of working nicely with just these couple tools, couple libraries that I introduced above. And you can see on an example that we will be actually able to very quickly search 100,000 items and return similar items.\n",
    "\n",
    "Save time instead of generating the representations for our images, I will download the generated representations from the repository and we can then go quickly to the search problem.\n"
   ],
   "metadata": {
    "id": "n7u6TxBO5kWx"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from classification.generate_clip_img_emb import generate_image_embeddings\n",
    "import os\n",
    "\n",
    "# Generate embedding for the whole test set in 13 minutes\n",
    "# embeddings_array = generate_image_embeddings(df, all_into_memory_only=True)\n",
    "\n",
    "# Or download and unzip in 3 minutes\n",
    "!wget --timestamping https://huggingface.co/datasets/glami/glami-1m/resolve/main/embs-clip-visual.zip\n",
    "!unzip -qn embs-clip-visual.zip -d $EXTRACT_DIR/\n",
    "\n",
    "# And load embeddings in 1.5 minutes\n",
    "from utils import chunker, load_embeddings\n",
    "from load_dataset import CLIP_VISUAL_EMBS_DIR\n",
    "embeddings_array = load_embeddings(df, CLIP_VISUAL_EMBS_DIR)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t5Zb5_9geHTe",
    "outputId": "3c21aae4-8769-4d18-e7e1-d3190c57f5d5"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      "Connecting to huggingface.co (huggingface.co)|3.231.67.228|:443... connected.\n",
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      "Location: https://cdn-lfs.huggingface.co/repos/9c/21/9c2175150bdb8fa4a3b9c6faec05eedf3bcc8fb92f9a5aad3546a6df731d640c/dea7d24c5c59e70cce67ffac6ec768a0a1dfe7b667b099b098f4a9ff7480fb23?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27embs-clip-visual.zip%3B+filename%3D%22embs-clip-visual.zip%22%3B&Expires=1676278086&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzljLzIxLzljMjE3NTE1MGJkYjhmYTRhM2I5YzZmYWVjMDVlZWRmM2JjYzhmYjkyZjlhNWFhZDM1NDZhNmRmNzMxZDY0MGMvZGVhN2QyNGM1YzU5ZTcwY2NlNjdmZmFjNmVjNzY4YTBhMWRmZTdiNjY3YjA5OWIwOThmNGE5ZmY3NDgwZmIyMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2NzYyNzgwODZ9fX1dfQ__&Signature=k52POUQFag-qWlAvfCyUeuHjXQBKTdzfrQQ-JGku3t6MbrhGRgHdDO97D4ysCTQyZVFS7tJ9f-IuNWB1UD1Pc2WJeUUEKNaPMNLBlw5yWf9YgmENxf8PXqKPOORmZnpS9pwb613dbKhGnZkkzutRG-9w-gQvLXaXBYNLcaBDSYnOR9RioM0drS7sX76BIAFIZ%7EcGHQkK1-go0t-p0C6frNGW6GhbW25LyIVQd3sLuFyOsKY-sbrrWxdG1sQ%7E-STwV3uFTsJlgtHGcArBynioAkEbLGYAt6OYjCSxM958K974jpqi5vE-J0B1kKoQzo1g%7E99uZh55ujpFcfvyvG8Ctw__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
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      "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 13.227.254.52, 13.227.254.33, 13.227.254.123, ...\n",
      "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|13.227.254.52|:443... connected.\n",
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      "Length: 1502227768 (1.4G) [application/zip]\n",
      "Saving to: ‘embs-clip-visual.zip’\n",
      "\n",
      "embs-clip-visual.zi 100%[===================>]   1.40G  17.2MB/s    in 89s     \n",
      "\n",
      "2023-02-10 08:49:35 (16.1 MB/s) - ‘embs-clip-visual.zip’ saved [1502227768/1502227768]\n",
      "\n"
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    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 114/114 [01:48<00:00,  1.05it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Create Approximate Nearest Neighbor Index For Fast Search\n",
    "\n",
    "Now that we loaded the representations, the embeddings, the points in space into memory, we will build our index, our scan index and use it in the next cell to do search.\n"
   ],
   "metadata": {
    "id": "gfqYuGsu5wI9"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from scann.scann_ops.py import scann_ops_pybind\n",
    "from scann.scann_ops.py.scann_ops import ScannSearcher\n",
    "\n",
    "num_leaves = int(np.sqrt(len(embeddings_array)))\n",
    "n_neigh = 10\n",
    "\n",
    "\n",
    "index_builder = scann_ops_pybind.builder(\n",
    "    embeddings_array,\n",
    "    num_neighbors=n_neigh,\n",
    "    distance_measure=\"dot_product\",\n",
    ")\n",
    "\n",
    "index_builder = index_builder.tree(num_leaves=num_leaves, num_leaves_to_search=2 * n_neigh)\n",
    "index_builder = index_builder.score_ah(\n",
    "            dimensions_per_block=2,\n",
    "            anisotropic_quantization_threshold=0.2,\n",
    "        ).reorder(2 * n_neigh)\n",
    "index: ScannSearcher = index_builder.build()"
   ],
   "metadata": {
    "id": "es4qACcxkNBE"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "Our index is created and stored in the index variable. Now I will select a sample from the 10 samples shown above. I will display the sample together with its text and image."
   ],
   "metadata": {
    "id": "iYnnHd8l3-eK"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Select an item\n",
    "sample_image_i = 2\n",
    "sample_item_idx = sample_df.iloc[sample_image_i: sample_image_i + 1, :].index\n",
    "print(sample_item_idx)\n",
    "sample_item_df = df.loc[sample_item_idx]\n",
    "\n",
    "from utils import public_dataset_to_html\n",
    "\n",
    "public_dataset_to_html(sample_item_df)"
   ],
   "metadata": {
    "id": "yxqyOTwtkOZ5",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "outputId": "4a92a350-f2bf-4b5c-f861-26aa02486016"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Int64Index([5906], dtype='int64')\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>item_id</th>\n",
       "      <th>image_file</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>318452</td>\n",
       "      <td><img width=\"150\" 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\"></td>\n",
       "      <td>Pánské lyžařské kalhoty KILPI RHEA-M červená</td>\n",
       "      <td>Kolekcia 2019   Pánske lyžiarske softshellové nohavice Kilpi RHEA-M s predtvarovanými kolenami pre lepšiu voľnosť pohybu. Nohavice majú dve hlavné vrecká na zipsy a jedno zadné vrecko na zips. Nohavice u nohavice sú rozopínajúce a majú snehové manžety. Nohavice sú tiež vybavené ochranou proti okopaniu. Pas sa dá regulovať suchým zipsom, zapínanie nohavíc je na zips a na dva patenty, traky odnímateľné. Priedušný a vodeodpudivý materiál Siberium 10 000 SRC SS     materiál Siberium 10.000 SRC SS vodeodolnosť 10 000 mm   cm2, priedušnosť 10.000 g   m2   24h   predtvarované kolená   2 hlavné vrecká na zips, 1 vrecko vzadu, tiež na zips   rozopínajúce nohavice s ochranou proti okopaniu   nastaviteľný pás pomocou suchého zipsu   odnímateľné traky</td>\n",
       "      <td>mens-pants</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now we will use our index to search most similar products in the entire test set of 100,000 items. And we will do that in milliseconds."
   ],
   "metadata": {
    "id": "G5K7L0KJ4ave"
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "id": "EWX33Zee4f8G"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Search for visually similar items\n",
    "\n",
    "idxs, sims = index.search(embeddings_array[int(sample_item_idx[0])], final_num_neighbors=4)\n",
    "\n",
    "similar_df = df.iloc[idxs].copy()\n",
    "similar_df['similarity'] = sims\n",
    "print(sims)\n",
    "public_dataset_to_html(similar_df, extra_cols=['similarity'])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 926
    },
    "id": "4iHENhLkufhc",
    "outputId": "d00bf38c-d63a-4be7-98c4-f78918d9ab8e"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.9999166  0.96320164 0.93617594 0.93090725]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>item_id</th>\n",
       "      <th>image_file</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>category_name</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>318452</td>\n",
       "      <td><img width=\"150\" 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\"></td>\n",
       "      <td>Pánské lyžařské kalhoty KILPI RHEA-M červená</td>\n",
       "      <td>Kolekcia 2019   Pánske lyžiarske softshellové nohavice Kilpi RHEA-M s predtvarovanými kolenami pre lepšiu voľnosť pohybu. Nohavice majú dve hlavné vrecká na zipsy a jedno zadné vrecko na zips. Nohavice u nohavice sú rozopínajúce a majú snehové manžety. Nohavice sú tiež vybavené ochranou proti okopaniu. Pas sa dá regulovať suchým zipsom, zapínanie nohavíc je na zips a na dva patenty, traky odnímateľné. Priedušný a vodeodpudivý materiál Siberium 10 000 SRC SS     materiál Siberium 10.000 SRC SS vodeodolnosť 10 000 mm   cm2, priedušnosť 10.000 g   m2   24h   predtvarované kolená   2 hlavné vrecká na zips, 1 vrecko vzadu, tiež na zips   rozopínajúce nohavice s ochranou proti okopaniu   nastaviteľný pás pomocou suchého zipsu   odnímateľné traky</td>\n",
       "      <td>mens-pants</td>\n",
       "      <td>0.999917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1098560</td>\n",
       "      <td><img width=\"150\" 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\"></td>\n",
       "      <td>2AS Renkli Kadın Spor Kayak Pantalonu</td>\n",
       "      <td>2AS Kadın Asama Kadın Kayak Pantolonu Kırmızı Renkli Morhipo'da online satışta! 2AS Spor Kayak Pantalonu modelleri ve indirimleri Morhipo'da. Spor Kayak Pantalonu fırsatını ve indirimlerini kaçırmayın. Ürün adı: 2ASW18001003REDTRE 2AS Asama Kadın Kayak,Renkli,M, Barkod: 8680618027006, MPN: 2ASW18001003REDTRETREM</td>\n",
       "      <td>womens-pants</td>\n",
       "      <td>0.963202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18046</td>\n",
       "      <td><img width=\"150\" 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\"></td>\n",
       "      <td>CARCOOLKA Šusťákové kalhoty K20012</td>\n",
       "      <td>Dětské kalhoty jsou vhodné na podzimní a jarní období, ideální pro běžné nošení ve volném čase, ale i pro sportoví a outdoorové aktivity. Kalhoty jsou velmi praktické z hlediska funkčnosti jejich materiálu, velmi pohodlné, v pase stahnutelné gumičkou, též nohavice stahnutelné gumičkou. Na bočních stranách mají bezpečností reflexní proužek. Materiál je velmi praktický, voděvzdorný, neprofoukne, nepromokne, ale zároveň je velmi prodyšný a nenáročný na údržbu. Kalhoty vyrábíme s různým provedením vzoru. Velikosti: 134 Složení materiálu: líc: 100% polyester rub: 100% bavlna</td>\n",
       "      <td>boys-pants</td>\n",
       "      <td>0.936176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20855</td>\n",
       "      <td><img width=\"150\" src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAQDAwMDAgQDAwMEBAQFBgoGBgUFBgwICQcKDgwPDg4MDQ0PERYTDxAVEQ0NExoTFRcYGRkZDxIbHRsYHRYYGRj/2wBDAQQEBAYFBgsGBgsYEA0QGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYGBj/wAARCAEqAOQDASIAAhEBAxEB/8QAHQABAAAHAQEAAAAAAAAAAAAAAAIDBAUGBwgJAf/EAE4QAAEDAgQDBQQFBgsECwAAAAEAAgMEEQUGEiEHMUEIEyJRYTJxgZEUoaLB0RcjQmKxshUWJDNSY3KCksLhJSdT8Ak2N0NERXODo9Lx/8QAHAEBAAIDAQEBAAAAAAAAAAAAAAYHAwQFCAIB/8QAQBEAAgECAgUHCAkDBQEAAAAAAAECAwQFEQYSITGxB0FRYXGBwRMiYnKRobLRFBUjJCUyMzRSQoLhU2OSosLw/9oADAMBAAIRAxEAPwDv5ERAEREAREQBERAEVpzLj0GWstVGMVETpWxWDYmmxe4mwF+nNciZ37bGbMkcRqfBKjIWBYhSuiM0kNJiUpqNJB0XJYGsOwNiHeHyuFrO8oqurVy89rPLqRlVGbpuql5q2HaKLz3Pb/zQKoDEssCieLCWKmMb42n9Uv8AERy5n5La+Vu2Wx1dTVmeMoVWE5VnYYoswFukvntqa3udROktDjqv05brX+soxrKjVhKObyTeWT7Nr4ZmT6M3DWi08t+Wez3HWKLFsP4j5LxTDKfEKLHIpKeoibNE8xvGprhcHceRCqjnbKw/85g+TvwXx9d4du+kQ/5x+Y+hXH+nL2Mv6LEouKGQJ5ZIqbNVBUPikEUjYHGQxvPJrg0HSfeql2fcrNv/ALRLv7ML/wAF81cew2l+pcwX90fmfsbG4l+WnL2MyRFrzOXF/A8rZUfjNBhmJ5ilZI1jqHDIx34ab3fZ5bdo62ud+S5fxj/pCZXZglwrLvC8udG8tL67ErP2ve7Gs2ItuLrZtcRtrttW1RTy/i0/ethjqW9Wl+pFrt2HcaLz/rO25xTx51RgWEYLlnBKjEDFSYfiLXve+kndI0aniTUyRttTbaRYkHey3Hw54qcRn0WESY5maPH3Vj2tn+k0EMHdkv0ua3ug3lvYm61cUxu1w2cKdw3nN5LJZma1satzGUqe5HTqIEXXNMIiIAiIgCIiAIiIAiIgCIiAIiIAiIgNNdpfNtPk/g3LilU+0cb9YZ1kcAQ1o9SXAf8A4vLufF6jFqirxnF5AKyeV0z5nNN3km4sSOQHhFugXYHb1zjLiGaMscM6N4LIonYpV6AS4ucXMjaQOgDXu+IPRch4lNiNJhvdREwtiAMcjHuA08tB1DxHqta3sowuKl09spZLsS5u95t93QZp1m6caS3LiWWgoW5rzlh2FU1M5jZ5B3m+pzmjdxPwXQPaDliw7hBlrKMNOXVctS2UMAA0hjCD9b2iyxPs35VnxvNmI5ifGDHTgRxveNjvdwHv2V77Q+IPpOKmVxPE2QRN71sLnaA89824v0B0t3UGxO9V7pHQtYvNUVKX92q38jtW1HyWHzqP+vJd2Z09lHD3YRkXBsKBv9EoYYLjrpYB9yuWKVFNDl+ulxKrFHRtp3mepdJ3YiZpILtXSw3umHd9NSxu7uNo0jqT06KTmPLVBmnKeIZcxiJz6GuhdDMI3lrtJ6g9DcAqgXOM7nXrPJOWba379uXWTdQap5Q6DS/ArJmS8GkxXFck54lzKyeaKkkLo+67gBxeNQ5ucQPa67rf9nEeKywbJOQcHyB3WFYJE8idwllmlIL36G6Wg2AAtqPzKzvxEdAulpNfK9vpVoVHOLyyckk2sstqSS5ujt2mCwoOhRUJJRfQtxRVMQkicxzbgiy89+L2Az5U414jpD4WVUhnjcNiC4m9rfrA/Neh8mrSRc39Fy52rMny1GBw5njHipni/mQdnfsafmpNya4qrPE1Rk/NqLV7+b3nN0gt/K22st8dpoNjYqvA4KuonLpo9Qa0ObAdTRzDne1bY7LsPs549FmvBcv0jqqEYjFVNjqIHvDXtcJAdWkm9jzHvXHuXnRNwaonkdCdgQ1wLiGnYkho1W3HkFuPs+4pLQcScawsyU4mkp4aqF8TNJ1RuFrX32DgbE9FcWmFnCpZK6ks3Sals51nk17NvcRbCK0o1XRT2TWXyPU0IrXlvFDjeU8PxZzQ11TA2RzRyBI3+u6uikVKpGrBVIbms13nMlFxbi96CIiyHyEREAREQBERAEREAREQBERAEPIosN4q8QMM4Y8JcZzjiU8bPocDvo0bz/P1BBEUYHUl1vhc9EB5w9ojMs+OdrjO8kuIRd3T1Iw+LxtAAiYGC19jY6ri/UrS2Nl7WxQ0jX3kb3ZLGlkc0mq12tvbnYbKmxTFJ8Sxaqr8QJfUVEz5pZDvqe5xc4/MlV+TsOq8Yz5g9Bhbbzuq43Bw3DLOBvb/AJ6r8r1VRpSqS3JN+w+6cNaSS5ztThBklmTOGeHYb3VpzGJZz1c87m6zPEMBwnGJY5MUwqgqzF/NmeBshbvfYkE9FVwtswRg3DRYKribuvI15f1q9xO6lLzpNvPtLNo0YwgqaWxEymjEUQY3Zo8lV29LqSwWt0VQOZXKlteZuQ3FukY7+MULj/wHD46lVkWCglaTitO/yY4fsU4jYrJLal2H7LmKdw2WK59y9TZlyLiWFzwiQy072NuL2JaRdZYW3O26lzQtNPI14uHNLTbnvssttXlb1Y1YPJppmGpBTi4vnPNOjfUYHXTQPiDpoS6BzS4gXa61zpIvy5LNOE+YajD+M+DVE+gRVEz6Z7R4Wt70W1fMN5rH895fqsoZ/wAVwmrjeC2ocWC3MX5+4ix+Kx2CsqYqyOohcI3xvD2nnYg3C9dThDErFxTzjUjv7VvKwjJ29bWy2xfBnshwgxFtbw0ghDw40sj4bX5C+pv1OWeLn3sjZuoc4cJKmvow2N0UkUU8F94pQyzh7jYEHyK6CWvgkKlOxpU6v5orVf8Abs8D6vXF15yhubz9u0IiLqGqEREAREQBERAEREAREQBERAF509uPiscz8UIOHmF1OrDMvb1Ok7SVjx4r/wBhtm+8uXf+aq/GMLyVimI5fwl2LYpBTPkpaFrw0zyAHS25IHNeN2bI8ckzdic+Ym1DcWlqZJKxtS0tk75ziX6gdwbk7L6ifqMaduT1C6w4A8MKLCMKgzVXQOOIysu0v5Mv0A9ARv71ojhZkifO/EOnw/SRSU/8oqpCNmsB2HvJ2C7nw+lgoqOOlpmBkUTQ1o9Aqr5S9IHQpRw6hLJy2yy6OZd5JcAsdduvNbFuLhE2wVZEw23spEIuLlVTL35WCoiZMYkxgGpTGnxOULRYr4HWeQeu6+FHaszLHnPjxethNuQd9yjNgN1C8fyph/Vd9yiJ6Dms2qfknuIOpUDhqFlOtsoLbr9hSz2sxuRpbjnwxo82ZYnxano4xiUEZ0zAeLbl8Oh964ldG+KV0T2Fr2khzT0I5henNbG2SnfG9ocxwsWnquHePPD+TJ3EB1fTRk4bid5YXgbNePab7+vxVy8mOkD1pYXXl1wz968faRPSGy2K4gu35mddjbi5+TzjSzL2KVIjwPMZZRzl5s2GcE9zJz23JYT5P9F6eDcLw3gkfFKHscWuBuCDYj1C9S+yzx1j4t8Ov4JxhwjzPg0UcdX4r/So7aWzi+9yRZw6Osf0grjkucihv1ERfACIiAIiIAiIgCIiAIiIAiIgC0xx07OuU+MOX56ltNT4dmhjLUuKtbp1EcmTW9tp5X3I6eS3Ovh9k2QHCHDjht+TTCv4JqZIZsSlcZKueLdrnAkBoJ3LWgWHxPVbLpRqAsqLFRbHn9PD95Vwomdei8x6TVp3F5KrUebZY9lBQpKEdyK+Nthsp7fZUEbdlH7JsFFZRzN+LPtz0KhDiC9rrezcL64ncgL4x3Q7r4SaM0SbcFzXfqlLjmFS1DxE4BhPmR71DHMQ7crOuk+ZIrNVgSVDcEXXw+L3L7bwrKYimmdzHktf8TcmUmeuH9XgU+hlQT31LO4fzUwvpPuPI+hWfT+SttUy8LrbLYsK9S3uYV6TylFpp9hgrQjUg4SWxnn1h2T8w4lm4ZXoMIq6nF3TmnFFBGXyGQGxAA/by6r0G7LfZjxzhVi7s75uxXu8XnpXU7MKpH6o4WOIJ71/J7vCLAbDnclbA4G5ewamq8dx2DDKKPEKiSNktU2FoleLHm617ctutluZepsLvnfWsLhrLWX+Curqj5CrKnnnkERFvmsEREAREQBERAEREAREQBERAEREByxmSmfS5uq6eT24pXRm3UhzvxU6ilF9Pmq/PjAM/wCIFo2+lSb/APPvVvw6MGxJ3AXmLSVKF5OK5nkWLYvWpRfUXVgBC+25lfI2qbawUWlLI6SRJc6wKkCUBxAKqnNDgRZUFTTExPDSQbbL5g895lgUEuINlxSppWuBMTmXt0uL/gquN1yN1Z8Jwumq9WaI5Zi+sja1zHSksGi7RZnIHnc8z8ArzCzxgLaqqEXqQe7f28/vE+suTT4AvvoF8aLNXy/jt6L9MBInbvdUM7fzRurnKA4bKkkiu0g8llpPJmOSNj8FW2w3GHX/AO+jH2SfvW0lrXg5CGYJicnIuqGj5M/1Wyl6Z0Wz+q6OfQ+LK9xN53U//uYIiLvmgEREAREQBERAEREAREQBERAEREBztxcpayhzPPPRQNklfUOeGi13AsYQBf1usShxWrhqGRvw+RrifI7NJ92/Xy6LZXGCHRmSmfa2sB1/gR9yw2m1FvMrz9pbcUaGIVaVWipbXtzae3sJxhkJzoRlGWRcWG+4KnahbdUzCWtUTXXuSq5mlvJBEie7c2VJPM8AeKzQqh5sL3VJO0vjc1ou7oF8wSbRljHaSMKp+5wOmp2wOhiOuWNp6sL3EEeh3VTqDXgjf3LKs74THgZwinibpEeGRxW9Wkg/tWIRbxrrYzYSsb2pbvfF8Vn4mtQuFcUlVW5lwjlEjDYL7Y6TbmVIhBAVQ3fqtOLeW0/WQhthuoHM8LvcppO6gc4NaSVkjsPw2zwuibHklzw2xfUvJ9bAD7lmqxXhzHo4f0jre257/tH8FlS9SYBDUw2gvRXArW+edxN9bCIi65qhERAEREAREQBERAEREAREQBERAaf4zN/2jh7hz7v/ADFYDTbtC2DxmH8uwv8AWjePk4Fa+gIDQLrz3p7SbxOq11cCdYJL7tErmeHmvrgenJQx+IWuph5egVeSXMd6JJ3cCFMw1gmxykpnHZ08bfm4KS0kPPkrhluMS53wpjhcGqj/AHr/AHLYw6l5S6p0+mSXvR+znq05S6mZpxfgszC6gde8jP1H8VrWAg2W1+LsV8tUM1vYqSL+9h/Bakhdoiv+keSmPKFRUMbqvpUX/wBUvA5GBS1rGHVnxZXtIF1HGeZKpqZrnvOrkFWenkodHadN7CS8kA25qRKSITc2U558ZVFWSWjIv0W5Qp60kjFJ5JnQGSou6yFhbLWvAHfMkq/KgwOMRZZw+JosG00Y+yFXr1TYU/J21OHRFL3FaVpa1ST62ERFtGIIiIAiIgCIiAIiIAiIgCIiAIiIDVPGqB30DDKpvNjpB+wrVdNLqbdbm4wQ68nQzWvomt8x/otIU5LH6FRmn1NLEZ9aT9xNMClnbrv4l5hefNTw4l1iqSnN2qpZu7ZVnWSz2EhgfNgXBXfKA7zPuFC3KoB+QJVkc784fRZBkUB/ELDL7WkcfslbeBQ1sSt10zh8SPy6eVvUfovgzPeLX/UunF9vpbb/AOFy05CNr8ytzcWAP4ixE8xVx2+TlpuIgRG3NTDlKj+MP1Y+JydHn9yXaythc1kXmVNDwWeqo4nWbZRueVBqazOu2QTP0klW6Z5keRfnsqmYkgqTRQCfFqeE7iSVjD7i4BdexpqVSK6zVqyyTOncObowmmZ/RhYPshVK+MaGsDWiwGwC+r1JTjqxUegrVvN5hERfR+BERAEREAREQBERAEREAREQBERAYRxWaPyfvlc0Fsc7C65tsbj7wtERw+N1natJ5+Y81vnivB3/AAwrdzdj4ngeZ1gW+taBozPDUNBBdGR5bhUryj087zNLbqp+9/ImOj7+wfa/Au8TRo8KmNcWOKRBuu45FTHRg8zbyKqiSb3kmiU7nXeTpcPW2yyPIQH5QsNPTU79xyxpx7rULu1A9BcWV+yLOHcQMLd/W6fLm0hdHAPNxK2lza8PiR+Xaztqq9F8GbH4ri+QR6VUf7HLTEQJbc8lurinYZAcTyFRGfrK0mDZptbu78gbFS/lMX4usv4R4s42jn7LvfgVTANKhe62yiY5pjACgd7XuUDizrtEmUmxU/L2h2a8MjeLh1XGPtBUcztip+XH2znhbjz+lxgf4wurhLzuqef8lxNa4X2cuw6fHJECL1QitAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAxLibH3nC3FrEAsYx+/pI0rQlPO1+l1rhw3Pquhs+Rsl4b4wyQ2b9GJPwN/uXM8Mb6SpsHXYT8PQqoOUfzbmHQ4+/Nks0e2031PwRf9eki3JTXTWjVKx2pgBGyj/QI8lUElvRLIrMheJJHfmzur5lAPjzvhXeFl/pLBYc+asWiRwtG8NPqr7k2O2ecLZKWazUtO2/LdbeE5u/oJfzj8SFbZQqZ/wAXwNncVBfh3L6Txn61pOKeKVngYA4cwt48TSBw9qHO5CWI/aWhXWFXqYG2PIhTXlOh+LR9RcZHD0cf3N+s+CK9tzc3Up5duoonHuySog24JVdRlqnbazKCcndV2UIjNn/B2kbGrj5+hupT4GuJLuQV1yLEJuJ2EMA2bMX/ACa4rvYAlVvqMOmUeKNG982jN9T4HRqIOSL1KVsEREAREQBERAEREAREQBERAEREAREQFhzqx8nD7GGMNiaV+65gjlcHd24eHl7l1Zj8YmyriURF9VLIPslcrGPxi3UBVJykpwrUZ8zTXvJXo7k4Tj1lypZhp0k+4qqadXJUdHGCyx+tV3hjaqbqSWtsJXDNIiZYPv06q+ZSmYzPeFu8IH0ho35m+33rHCZnyhkR3O97cle8rU9854bTsdd5qYy559HArZwnON9Qkt+vH4kfdbbQqJ9D4G2uJEevhtiB56e7d8nhaBNm2I2F9gug+ITS7hrigF/Yadv7bVz9DZ0BY/xAHa6sDlQjlidN+gvikR/Rl52sl6T4Ims1AWIIVTELsuVKaHHm69uSmscGtIVYSJBkSpiA036K4ZAIdxPwpziQO9Ng3z0OVpqXtublX3hkY/yoUHh1uPeAC2zfA7dSbRWm5YhQ2f1R4o52Iyyt59j4HRA5IiL0+itwiIgCIiAIiIAiIgCIiAIiIAiIgCIiApsRbrwiqZa94Xi390rk2meTZjubRbddbVI1Ucw82EfUVyO02nItu11lV/KRT1o0X63gSTR6WTn3eJeoG2jBDh7lMINt1Io3EgE8lUyEFpsFR1SLUmTKLzRJZL3ct78hushyTI1+d8MDdTiahpc4Da6xuKESTeMeEb+9ZLkudr+IGFsbsxs4A9+638HS+sLdenD4kK/7ep6r4G389C/DrFR/U/5gucgXx1b4zyJXRXEA24a4sR/wh+8Fz9KGSCOZvM7FT/lReWI0vU/9SOBoz+1n63gipjPhvZSZpNINt1Nb7GykvaTfZVfBLPad+TKKVxfcnmsw4URtHEalcbE93Lb/AAH8QsSfHpu4lZhwk/OcSWOPJlLLYfL8VNdEoa2I0Nv9S4nJxN5W8+xm/kRF6OK/CIiAIiIAiIgCIiAIiIAiIgCIiAIiID4QCCCuRamIsxmpYDYCVwt/eK67XJ2Lt7vNOItAtapkH2iqy5Snq0aEl0y4IkWj22c12eJNpHabDoFctLXXIVvpAC291dI9Gg7dFRlzLbmTOkthSSXZGdPMq65Qe6LN+GkloP0hm45+0rVU+FpVXlp5ZmOil52qIz9oLYw2epcUp9Eo8UZJLWpzXU+BvHiB/wBm2Lf+kP3wuePFHI+P9G9wF0NxAIHDbFb9YwPthc+PGqWysXlRf4jS9RfFIjujP7WXreCKuHxNCnmK7SFIgaQ70VYTZhA5qqpPJ7CRItdXHpY7dZbwbic7iBM8/oUbz83NCxKtO1r7lZzwUi05pxF7jdwpAD8Xj8FN9B5OWKUIvp4Js42MbLao+o3aiIvSJX4REQBERAEREAREQBERAEREAREQBERAFylj7NOdMUZruPpcn7xXVq5SzQBDxFxiEG4FZL+8VW3KVHO0ov0nwJDo8/tJ9h9prjYcldISA1W+AtDBZVLXkHZUNW2smsNhKqyXXF7EqPD5WwVdO5p9mQOv7iqaocXSaQfeVTzVLKdut7g1o6n3FZaKaayMtNrPJnQfEib/AHXYg9vJ4j+uRq0FE68gJW7eIc4PBd0twRI2m3B53c1aOguZLKxOUyWtiNN/7a4yI7o6tW1kvSfBF5iaC1fXyaWEX3Upjy1mygku5pNt1WKjt2nfbKOrOp1hzWxuCcDhi2MSvN3CKJp9LucfuWuZjpBPkto8EfHS41MeZkiH1OU/5P6Oti9KXMs/hZw8cnlaz7uJtlEReiCBhERAEREAREQBERAEREAREQBERAEREAXKOczp4o44L/8Ajpf3l1d0XKWeoz+UvG9PMV0h+tVxylfs6PreDO/o/wDqz7PEm0rA6BqqxEdOwVvw95EYDirnruywPxVB1c1Jk4hlkWyZpMhudgtIcceJLsEnp8o4RI36VVxu+lyDcwwuBbYeTnC+/Qe9bdzdjNBlXKmJZlxJ7jT0cJkMbObzya0e9xA+K4QxTGK/MGb6nGMRl11dXMZZCOQJOwHoBYD3KyuTvR6OI3DvK6zp0/Y5f43vuODjl+7en5KD86XA9Le0XmKtyj2Q6bGcMLRNTzYYGsd7MjdbLsd6EXC1pkbNOGZwy1TYzhkocyRoEsRPihkHtMd6g/MWPVZP2xZHU3YpoInGxfW4bGfgCf8AKuPuAedYsscSW4dWTaaDFw2me4mzY5b/AJtx8t/CT+sp5p3o3HErOV1SX2tNZ9qW1rxXs5zh4HiDt6nkpflk/YzscOGrSp1rhUzW2eC4i/UDop7neHYrzs0Tot9c9jWG2/mtp8DBqwfGZOhqI2j3Bn+q1HiDh3bgFuDgW0/xVxN9udWB8mD8VY/JyvxSC6pcDgY8/u0u7ibUREV/kHCIiAIiIAiIgCIiAIiIAiIgCIiAIiIB0XKmdH/71MeB5fTX/tXVfRco55Y5vFTHWna9Y8/A7quOUpfcqXreDO/o9+tLs8SVTnfa6rXOf3PhO6t9NdrQq9unuyXAkjoqJqbyaw3Gku0riho+GVDhrZjqrq9okF+bI2lxHzLVyvSW/hKAOdc6wCfiuje1NIG4VluI6RqqJ3kDewDGj71zT3ncTCUc2b/JeieTmioYJTkl+Zyb9uXBIgmOybu5LoS4Ho523hp7H+GNHIYvQD/45F52MNgS1xB5+Hz8wvQrtuyvd2PsAPSTGKHUf/YlP7V55NJaw+o81N4rYcdHePDHMNRmjhNgeNVW9TLThk7/AOlIwljj8S2/xWVySWbstQdm2rNTwalgc7X9GxKaMegIY79ritryPu7SL2815T0gs42uKXFCCyUZyy7M9nuLKsarqW0Jve0iiqfG035X+a3bwRj05Eq5P6dc/wCprQtJVRDBpW8+CjdPDaT1rZT9TVL+TeOeKZ9EX4HJ0geVt3o2KiIr6IUEREAREQBERAEREAREQBERAEREAREQDouWuIrAzi3jQ/rwfm0FdSrnji/l3EKbiDNjQopHUFVGz+UNbdocBYtJHI7X35qB8odvOrhsZRWerJN9mTR28Bmo3DT50YhCbsBBsVXMfdhvt6q202l0IaJHagLbEFV7GkC4eT7wqAqLaTmBzp2phpiyyQD7dSL+YtGub+672dkTQTre1o9bm33rovtUzllbliN8jAzRUPsTY3uwdei1nwd4d5h4l8XMAwXBcKqJ6R1dDLWVbI3PhpoGSB0jnv8AZHhBHmSQAvR+gKawKhn6XxMgGNv75Pu4I7Z7dcT6fso4JThtmsx2jYfS0EwC87GncN6X6r1A7ZWUsYzZ2Wq+LBMOqMQqsOr6bETT08Zke6Njy15DRubNeSbb2BXl+0xNmdFIHMe02cy+7T6tO4UwjuOUdO9lmr7zLOY8NNwIauKcAHo9haf3FvaaRkYIbu5cx9mCukhzzjuHMa5zZ8PbLa1rFkoA+p5XSZimeS572tv8V5x08tvJY5Wb3S1X7YrxzJ/gtTWs4LozXvJEpLnm5+a39wabbhkx39KqmP2rfctAvjjAIMhe4nkNrrovhVRT0PC+gjqIXwve6STQ9pabF5sSD5ixXY5NabeJTklsUHxRpaQtK3S6/mZmiIryIaEREAREQBERAEREAREQBERAEREAREQBfHNa5pa5oIIsQeRX1EBY63JuVcQ3q8v4e939IQhrvmLFUjeHmTGezgUI/vv/APssnRc+rhNjVetUoQb64p+BnjdVoLKM2u9mJycMOHVRVx1dZknAqyeJuiOWromTuYL3s0vBI3WSUdDRYdRspKCkgpadmzYYIxGxvuaLAKoRblKlClFQpxSS5lsRilJyecnmwsWzDwz4eZsdK/MuSMv4rJL7ctXQRSSH++W6vrWUosh8musC4C8H8tYpJiOAZAwfDqqSMxOlpmOYSwkEjnyuB8lfvydZK1ajl6lcf1tRH1lZOi062HWteflKtKMpdLim/a0ZoXFWC1YyaXaW6iy/gWGlrqDB6Gmc3k6KBrT87XVxRFsU6UKS1acUl1bDFKTk82wiIsh+BERAEREAREQBERAf/9k=\"></td>\n",
       "      <td>Dětské zimní lyžařské kalhoty KILPI ELARE-JG žlutá</td>\n",
       "      <td>Kolekce 2019 20   Dětské zimní technické kalhoty KILPI ELARE-JG jsou voděodolné, prodyšné a pružnější díky použitému materiálu s membránou SIBERIUM 5000 CT. Kalhoty mají podlepené švy, nastavitelné, odnímatelné kšandy. V nohavicích mají sněhové manžety a zároveň zpevněnou část jako ochranu nohavic proti okopu. Nohavice lze po bocích rozepnout, regulovat pas za pomoci suchých zipů. Dvě hlavní kapsy na zipy a reflexní prvek pro lepší viditelnost. Kalhoty děti využijí na lyžování a městskou koulovačku.     voděodpudivý, prodyšný, strečový materiál SIBERIUM 5000 CT  podlepené švy  sněhové manžety v nohavicích  ochrana nohavic proti okopu  nastavitelné, odnímatelné šle  regulace pasu suchým zipem  rozepínání nohavic  reflexní loga  hmotnost 500g  logo Kilpi</td>\n",
       "      <td>boys-pants</td>\n",
       "      <td>0.930907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ]
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Above we can see examples of similar products. We can see that indeed the first, the most similar item is the item itself, which is absolutely expected. We don't get 100% similarity because of a numerical precision most likely. Then the second and third item are both red pants. where the second item has a similar shape and it's generally the most similar. Then the third item has a similar color but different shape. And the fourth item has completely different color but it has again, more similar shape.\n",
    "\n",
    "We can see that it disregarded completely the last item difference in language and we can see that this did search across all the languages without any additional input.\n",
    "\n",
    "We haven't trained this model for this. Despite that, we implemented search, and categorization, which is amazing."
   ],
   "metadata": {
    "id": "GQXsnTxC4heI"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Questions?\n",
    "- Where you are perhaps unclear or feel like you are missing in understanding?\n",
    "- Can we help to make the dataset for useful for your replicable research?\n",
    "- If you are reading this post the event, please contacts us on via email or one of the social platforms below.\n",
    "\n"
   ],
   "metadata": {
    "id": "BMXwv0ev86ez"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Experiment with The Full Dataset!\n",
    "- [Recording of walk through on Youtube here](https://youtu.be/jLFJObPWU1U)\n",
    "- [GLAMI-1M Code](https://github.com/glami/glami-1m)\n",
    "- [GLAMI-1M Paper](https://bmvc2022.mpi-inf.mpg.de/0607.pdf) and [Supplementary](https://docs.google.com/viewer?url=https://github.com/glami/glami-1m/raw/main/media/GLAMI-1M%20Supplementary.pdf)\n",
    "- [GLAMI-1M Blog Post](https://medium.com/@glami-engineering/glami-1m-multilingual-image-text-fashion-dataset-a72691b6fedb)\n",
    "- [GLAMI-1M Benchmark at Papers with Code](https://paperswithcode.com/paper/glami-1m-a-multilingual-image-text-fashion-1)"
   ],
   "metadata": {
    "id": "mq_3EaKn9W0Q"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Attend or Present Future Meetups!\n",
    "\n",
    "[Miton Times Meetups](https://www.meetup.com/miton-times/)"
   ],
   "metadata": {
    "id": "wSjQ-aa-9H-M"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Post author: [Vaclav Kosar](https://vaclavkosar.com/)"
   ],
   "metadata": {
    "id": "EYyQov_BIZ6G"
   }
  }
 ]
}