{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bring Your Own Data (BYOD)\n", "\n", "In this guide, we’ll walk you through how to integrate your own dataset into a LEIP Design recipe. We will start by reviewing the datasets we offer, followed by a step-by-step demonstration using the Road Sign Detection dataset from Kaggle. These steps can be applied to any dataset you choose to work with.\n", "\n", "This guide specifically focuses on **object detection** and is designed for integrating **detection datasets** into a LEIP Design recipe.\n", "\n", "First, we generate our pantry and create a recipe to work with, as shown in the [Getting Started tutorial](https://docs.latentai.io/leip/design/latest/notebooks/GettingStartedwithLEIPDesign/)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import leip_recipe_designer as rd\n", "\n", "# Define the workspace path\n", "workspace = Path('./workspace')\n", "\n", "# Build the pantry (do not rebuild if it already exists)\n", "pantry = rd.Pantry.build(workspace / \"./my_combined_pantry/\", force_rebuild=False)\n", "recipe = rd.create.from_recipe_id('44702', pantry=pantry, allow_upgrade=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before bringing your own dataset, you might want to explore the object detection datasets we provide. These can be a good starting point if you’re looking for a quick test setup." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "> Help: Dataset. \n", "> Ingredients that fit:\n", " Index Parameter Type Version UUID\n", " 0 BYOD from Url - PASCAL format data_generator.vision.detection.2d 1.0.0 da9ec6daa3a287173c17307eb727a033c356677976662fca84c1d0697c5960ef\n", " 1 BYOD from Url - COCO format data_generator.vision.detection.2d 1.0.0 0b9bca30d0fe77ee7734e56dc7291272c7e69894d3419ac64f70fe32add19f32\n", " 2 BYOD from Url - YOLO format data_generator.vision.detection.2d 1.0.0 104f9721404c4653f2eddbe04023b4858a72aff3cec2937e05c714ac1d2d91c8\n", " 3 BYOD from Url - KITTI format data_generator.vision.detection.2d 1.0.0 6305d7f53f4ae0bc7b669cb105172621f25daaed3934a96063a02dd489ebbf1a\n", " 4 BYOD - PASCAL format data_generator.vision.detection.2d 1.0.0 26c7b7a439669ce524a8d1128c51df0385bb09460eb26f174e61f5e0dd43ba8a\n", " 5 BYOD - COCO format data_generator.vision.detection.2d 1.0.0 f1b19f142448b17b02883bcc1663032ad8240928816f4f7db18ff86ba5651238\n", " 6 BYOD - YOLO format data_generator.vision.detection.2d 1.0.0 cac615b7fb149b5ea1a270c4782eb59202d40e20f77f3caa45e6ce40b221394c\n", " 7 BYOD - KITTI format data_generator.vision.detection.2d 1.0.0 2eeaaf51d0243838e482dc8f84d8ca3f9c180121c4e281f1758bca5e586b10a5\n", " 8 VAID (Overhead Car Detection) data_generator.vision.detection.2d 1.0.0 7a04de11614146936318448b40446d2dde40b8ddf2bd695e968778ab35ff36b2\n", " 9 Pothole Detection (data/sets/kaggle/detection/andrewmvd-pothole-detection) data_generator.vision.detection.2d 1.0.0 7965dc0775eedd4191928e5a9a376ab9617bc72fa0c032459fe2dfef5c90738c\n", " 10 COCO data_generator.vision.detection.2d 1.0.0 6e5bcaa5ddb35c0dd91cead96615c5859ea6f207cd0c6a121cc49cf9e85852db\n", " 11 Smoke (data/sets/url/detection/smoke-pascal-like) data_generator.vision.detection.2d 1.0.0 64b984853aefcba0f0bad3315f83bdd034215d6c8f12e833d8242a0ea3fb0d36\n", " 12 Fire and smoke (data/sets/url/detection/fire-and-smoke-coco-like) data_generator.vision.detection.2d 1.0.0 f95a890cc9c2102d0ae2268e2dd771ee6da23608650f0627d2ec459f1b23fc44\n", " 13 PASCAL VOC data_generator.vision.detection.2d 1.0.0 d91692760a3fa454bc5aad0ae247fc320c97ad5d0f57fa3d334bac23298bd953\n", " 14 COCO Car Detection (data/sets/kaggle/detection/coco-car-dataset) data_generator.vision.detection.2d 1.0.0 a7c5c41ec12a9c70f6b8264226968b7498bc9b5d2df8914b0aa5518fd3b661c1\n", " 15 Dials and gauges (data/sets/url/detection/dials-and-gauges-pascal-like) data_generator.vision.detection.2d 1.0.0 f4271f5edda5ead883767c87bd6564b1eccf4b9021ce36633ecf3d156956923e\n", " 16 Dice, Car, Battery Detection (data/sets/kaggle/detection/kitti-dice-car-battery-detection-new) data_generator.vision.detection.2d 1.0.0 0a0fdb2af87d607288594bc486b767480ac88fab9c04323faa5d74bb15ad9593\n", " 17 Ship Detection (data/sets/kaggle/detection/pascal-ship-dataset) data_generator.vision.detection.2d 1.0.0 6ab1c630fc6f71b72489354a10ca8fafe65570334a71f0a7734bf56325d0cc58\n", " 18 Zenodo wheat (data/sets/url/detection/zenodo-wheat) data_generator.vision.detection.2d 1.0.0 d9ab9d814df4321828ec0336c3f9ae926362eb977fa07d74871f1b054179d860\n", " 19 Attach FiftyOne Dataset data_generator.vision.detection.2d 1.0.0 7a12bd14e3ffe82142f62ddae46c9f8bdb0d92c6c0a9fb4c3ae5d50a49fd6908\n", " 20 Car Detection (data/sets/kaggle/detection/sshikamaru-car-object-detection) data_generator.vision.detection.2d 1.0.0 3ea6d7e169e173e42987169b929a6296175d18f891523463f8695ceabb1d3763\n", " 21 Face Mask Detection (data/sets/kaggle/detection/andrewmvd-face-mask-detection) data_generator.vision.detection.2d 1.0.0 f5737272f8595201f098a40490631b89416f20a3a9c6112ec3421405a698ea2b\n", " 22 Dice Detection (data/sets/kaggle/detection/pascal-dice-dataset) data_generator.vision.detection.2d 1.0.0 ee99df1f35e58cf28bf78049eda3da6a42df44a507a86d23cf66890d05b934ef\n", " 23 Fruits Detection (data/sets/kaggle/detection/mbkinaci-fruit-images-for-object-detection) data_generator.vision.detection.2d 1.0.0 8ecee62ebce897e400bb6d78e57e8724e83e943ba41b5687b6600e0a191cbe99\n", " 24 Smoke Detection (data/sets/kaggle/detection/pascal-smoke-dataset) data_generator.vision.detection.2d 1.0.0 f0fe4e825584c227287d20e0b3b870aa238e31af4e269cbb28183402d2d6245a\n", " 25 Composite - Mosaic data_generator.vision.detection.2d 1.3.0 f2ac92f31f08c8219590ea89a37d9b9b1bc21160af22a70b524b70c5ef80e140\n", " 26 Composite - Random subset data_generator.vision.detection.2d 1.1.0 5c1fb1649a698eeaa89b459ed07646065b56b59f7e2c932ee722a9cc0039a9af\n", " 27 Composite - Data joiner data_generator.vision.detection.2d 1.1.0 413dbe101b5a31107994d52fe45ad2ed7fd55623df05c4cff640227392512ff8\n", " 28 Composite - Matting data_generator.vision.detection.2d 1.2.0 495590fd21e17c66705e69820f4416bf1c46bf08e01c6c47d3713f054b4f0b58\n", " 29 Composite - Class selector data_generator.vision.detection.2d 1.1.0 d11aa4d1d87054674999de3bafb22370582bb73e179f0474ec8b9d25b201793f\n", " 30 Unlabeled Dataset data_generator.vision.detection.2d 1.0.2 8f26f69bbdbdd5c8bd8d8c960d9449c5a415507383dc1b27a545f37c97da49f9\n", "> Use recipe.assign_ingredients('data_generator', ingredient_name) to add it to the recipe.\n", "> Or alternatively, use recipe['data_generator'] = ingredient_id." ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recipe.options(\"data_generator\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Assigning Ingredients to Your Recipe:**\n", "\n", "To assign a data generator to your recipe, use the `assign_ingredients` method. This approach is recommended when building a recipe from scratch using one of our provided datasets." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'choice_id': 'a7c5c41ec12a9c70f6b8264226968b7498bc9b5d2df8914b0aa5518fd3b661c1',\n", " 'choice_name': 'COCO Car Detection (data/sets/kaggle/detection/coco-car-dataset)',\n", " 'synonym': 'data_generator',\n", " 'parent': 'Basic Adaptor',\n", " 'slot': 'slot:module.dataset_generator',\n", " 'path': ['slot:data', 'slot:module.dataset_generator']}]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recipe.assign_ingredients('data_generator', \"COCO Car Detection\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more details on initializing an empty recipe for your tasks, refer to the [Recipe Creators documentation](http://docs.latentai.io/leip/design/latest/content/reference/recipe_creators/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Note:** The `assign_ingredients` function is best used when creating a new recipe, as it clears and initializes preprocessing steps such as augmentations from scratch.\n", "\n", "If you are modifying one of our pre-validated \"golden recipes\" and wish to retain advanced augmentations like mosaicing that contribute to optimal performance, use the `replace_data_generator` method instead:\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skipped downloading goldenrecipedb with name \"xval_det\" and variant \"Xval0.3\" (0), as it already exists.\n", "This is the Cross-validation volume. Available methods are- \n", "get_golden_df \n", "describe_table\n" ] } ], "source": [ "recipe = rd.create.from_recipe_id('44702', pantry=pantry, allow_upgrade=True)\n", "data = rd.helpers.data.get_data_generator_by_name(pantry=pantry, regex_ingredient_name=\"COCO Car Detection\")\n", "rd.helpers.data.replace_data_generator(recipe, data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example Dataset: Road Sign Detection (Kaggle)\n", "The steps below will help you retrieve and set up the Road Sign Detection dataset. You can download it directly from [Kaggle](https://www.kaggle.com/datasets/andrewmvd/road-sign-detection).\n", "\n", "#### Steps to Use Your Own Dataset:\n", "1. **Download your dataset**:\n", " - If using the Road Sign Detection dataset from Kaggle, navigate to the dataset page, log in with your Kaggle credentials, and click \"Download.\"\n", " - Unzip the downloaded file and place the dataset in a local directory.\n", " \n", "2. **Set the `root_path` for your dataset**:\n", " - After unzipping, set the `root_path` in your code to point to the folder containing your dataset.\n", " - Example for the Road Sign Detection dataset:\n", " ```python\n", " root_path = \"/path/to/road_sign_detection_dataset/\"\n", " ```\n", "\n", "3. **Supported Dataset Formats**:\n", " - LEIP Design supports various formats such as YOLO, COCO, and PASCAL.\n", " - You can also [integrate datasets from FiftyOne](https://docs.latentai.io/leip/design/latest/content/reference/data_helpers/#leip_recipe_designer.helpers.data.attach_fiftyone_data_generator).\n", "\n", " If your dataset is in any of these formats, you can easily ingest it into LEIP Design using the provided helpers.\n", "\n", "4. **Ingest the Dataset into the Recipe**:\n", " - Once the dataset is prepared, you can attach it to the recipe using our [data ingestion helpers](https://docs.latentai.io/leip/design/latest/content/reference/data_helpers/#format-specific-data-generators):\n", " ```python\n", " data = rd.helpers.data.new_pascal_data_generator() # fill based on docs\n", " rd.helpers.data.replace_data_generator(recipe, data)\n", " ```\n", "\n", "### BYOD Example: Road Sign Detection Dataset\n", "For convenience, if you are using the Road Sign Detection dataset, you can mirror it by running the following command:\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create a new data generator for the Pascal VOC dataset format - ensure root_path is set\n", "data = rd.helpers.data.new_pascal_data_generator(\n", " pantry=pantry,\n", " root_path=\"${paths.cache_dir}/road-sign-data\",\n", " images_dir=\"images\",\n", " annotations_dir=\"annotations\",\n", " nclasses=4,\n", " is_split=False,\n", " trainval_split_ratio=0.80,\n", " trainval_split_seed=42,\n", " dataset_name=\"road-sign-data\",\n", " download_url=\"https://s3.us-west-1.amazonaws.com/leip-showcase.latentai.io/recipes/andrewmvd_road-sign-detection.zip\" # skip if pre-downloaded\n", ")\n", "\n", "rd.helpers.data.replace_data_generator(recipe, data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "recipe[\"data_generator\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Additional Resources:\n", "- [Supported Dataset Formats](https://docs.latentai.io/leip/design/latest/content/reference/data_helpers/#format-specific-data-generators)\n", "- [FiftyOne Integration](https://docs.latentai.io/leip/design/latest/content/reference/data_helpers/#leip_recipe_designer.helpers.data.attach_fiftyone_data_generator)\n", "\n", "Once your dataset is loaded, you can proceed to training the recipe just like any other dataset supported in LEIP Design, as shown in the [Getting Started tutorial](https://docs.latentai.io/leip/design/latest/notebooks/GettingStartedwithLEIPDesign/)." ] } ], "metadata": { "kernelspec": { "display_name": "main", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.20" } }, "nbformat": 4, "nbformat_minor": 2 }