Examples¶
Welcome to the 3LC tutorials! These hands-on guides will walk you through the essential workflows for working with computer vision data using 3LC. The tutorials are organized into four progressive sections, taking you from creating your first tables to building complete end-to-end machine learning pipelines.
Whether you’re working with images, videos, 3D point clouds, or various annotation formats like COCO and YOLO, these tutorials will help you get the most out of your data.
[
{
"title": "Ingest PandaSet autonomous driving dataset",
"blurb": "This notebook shows how to load 3D point clouds, 3D oriented bounding boxes and semantic segmentations from the PandaSet dataset into a 3LC Table.",
"path": "1-create-tables/3d/pandaset/load-pandaset.ipynb",
"thumbnail": "images/pandaset-light.png",
"tags": [
"3d",
"lidar"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials",
"dataset_name": "pandaset",
"run_name": null,
"table_name": "pandaset",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials/datasets/pandaset/tables/pandaset"
},
"include_in_docs": true,
"include_in_tests": false,
"include_in_public_examples": true
},
{
"title": "Create a Table from 3D points",
"blurb": "Create a 3LC Table directly from row data containing 3D point cloud information using the Mammoth dataset for 3D object analysis.",
"path": "1-create-tables/3d/write-mammoth-table.ipynb",
"thumbnail": "images/mammoth.jpg",
"tags": [
"3d",
"dimensionality-reduction"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Mammoth",
"dataset_name": "Mammoth",
"run_name": null,
"table_name": "mammoth-10k",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20Mammoth/datasets/Mammoth/tables/mammoth-10k"
},
"include_in_docs": true,
"include_in_tests": true,
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},
{
"title": "Create Custom Table",
"blurb": "Create a custom 3LC Table containing multiple data types with defined schemas and dummy data for demonstration purposes.",
"path": "1-create-tables/create-custom-table.ipynb",
"thumbnail": "images/create-custom-table.jpg",
"tags": [
"schema"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Cats & Dogs",
"dataset_name": "cats-and-dogs",
"run_name": null,
"table_name": "good-dogs-and-bad-dogs",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20Cats%20%26%20Dogs/datasets/cats-and-dogs/tables/good-dogs-and-bad-dogs"
},
"include_in_docs": true,
"include_in_tests": true,
"include_in_public_examples": true
},
{
"title": "Create Semantic Segmentation Table",
"blurb": "Create a 3LC Table for semantic segmentation tasks using paired images and grayscale mask files from the ADE20k dataset.",
"path": "1-create-tables/create-semantic-segmentation-table.ipynb",
"thumbnail": "images/ade-20-semseg.jpg",
"tags": [
"semantic-segmentation",
"ade20k"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Semantic Segmentation",
"dataset_name": "ADE20k_toy_dataset",
"run_name": null,
"table_name": "ADE20K-semantic-segmentation",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20Semantic%20Segmentation/datasets/ADE20k_toy_dataset/tables/ADE20K-semantic-segmentation"
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"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Create Table from Image Folder",
"blurb": "Create a 3LC Table from a folder structure containing images organized by class labels for image classification tasks.",
"path": "1-create-tables/create-table-from-image-folder.ipynb",
"thumbnail": "images/create-image-classification-table.jpg",
"tags": [
"classification"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Cats & Dogs",
"dataset_name": "cats-and-dogs",
"run_name": null,
"table_name": "initial-cls",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20Cats%20%26%20Dogs/datasets/cats-and-dogs/tables/initial-cls"
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"include_in_docs": true,
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{
"title": "Create Table from PyTorch Dataset",
"blurb": "Convert a PyTorch Dataset into a 3LC Table using the built-in conversion method.",
"path": "1-create-tables/create-table-from-torch-dataset.ipynb",
"thumbnail": "images/from-torch.png",
"tags": [
"pytorch",
"cifar-10"
],
"parameters_cell_source": "",
"project_meta": {
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"table_name": null,
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"include_in_docs": true,
"include_in_tests": true,
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},
{
"title": "Create a Table from video frames",
"blurb": "Create a 3LC Table by extracting individual frames from video files in the UCF11 action recognition dataset.",
"path": "1-create-tables/create-table-from-videos.ipynb",
"thumbnail": "images/create-video-thumbnail-table.jpg",
"tags": [
"video"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Create Tables",
"dataset_name": null,
"run_name": null,
"table_name": "UCF YouTube Actions - Frames",
"object_url": null
},
"include_in_docs": true,
"include_in_tests": true,
"include_in_public_examples": true
},
{
"title": "Create Custom Instance Segmentation Table",
"blurb": "Create a 3LC Table from custom RLE annotations using the Sartorius cell instance segmentation dataset with hundreds of cell instances per image.",
"path": "1-create-tables/instance-segmentation/create-custom-instance-segmentation-table.ipynb",
"thumbnail": "images/cell-segmentations.jpg",
"tags": [
"instance-segmentation",
"medical"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Cell Segmentation",
"dataset_name": "Sartorius Cell Segmentation",
"run_name": null,
"table_name": "initial",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20Cell%20Segmentation/datasets/Sartorius%20Cell%20Segmentation/tables/initial"
},
"include_in_docs": true,
"include_in_tests": false,
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},
{
"title": "Convert Semantic to Instance Segmentation",
"blurb": "Transform a semantic segmentation dataset into instance segmentation format by separating individual object instances within each semantic class.",
"path": "1-create-tables/instance-segmentation/create-instance-segmentations-from-image-masks.ipynb",
"thumbnail": "images/ade-20-semseg.jpg",
"tags": [
"instance-segmentation",
"semantic-segmentation"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Create Tables",
"dataset_name": "ADE20k_toy_dataset",
"run_name": null,
"table_name": null,
"object_url": null
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"include_in_docs": false,
"include_in_tests": false,
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{
"title": "Import instance segmentation dataset from multiple image masks",
"blurb": "In this tutorial, we will import the LIACi (Lifecycle Inspection, Analysis and Condition information) Segmentation Dataset for Underwater Ship Inspections, introduced in [this](https://ieeexplore.i...",
"path": "1-create-tables/instance-segmentation/create-instance-segmentations-from-masks.ipynb",
"thumbnail": "",
"tags": [],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": false,
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},
{
"title": "Create Table from COCO Instance Segmentation",
"blurb": "Create a 3LC Table from COCO-format dataset containing images with instance segmentation annotations for object detection and segmentation tasks.",
"path": "1-create-tables/instance-segmentation/create-table-from-coco-segmentation.ipynb",
"thumbnail": "images/instance-segmentation.jpg",
"tags": [
"instance-segmentation",
"coco"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - COCO128",
"dataset_name": "COCO128",
"run_name": null,
"table_name": "initial-segmentation",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20COCO128/datasets/COCO128/tables/initial-segmentation"
},
"include_in_docs": true,
"include_in_tests": true,
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},
{
"title": "Create Table from YOLO Instance Segmentation",
"blurb": "Create a 3LC Table from YOLO-format dataset with polygon-based instance segmentation annotations for modern object detection pipelines.",
"path": "1-create-tables/instance-segmentation/create-table-from-yolo-segmentation.ipynb",
"thumbnail": "images/create-yolo-table.png",
"tags": [
"instance-segmentation",
"yolo",
"polygons",
"beginner"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": false,
"include_in_tests": false,
"include_in_public_examples": false
},
{
"title": "Create Custom Keypoints Table",
"blurb": "Create a 3LC keypoints table from the Animal-Pose dataset with custom schema definitions for pose estimation tasks.",
"path": "1-create-tables/keypoints/create-custom-keypoints-table.ipynb",
"thumbnail": "images/animalpose.png",
"tags": [
"keypoints",
"animal-pose"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - 2D Keypoints",
"dataset_name": "AnimalPose",
"run_name": null,
"table_name": "initial",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%202D%20Keypoints/datasets/AnimalPose/tables/initial"
},
"include_in_docs": true,
"include_in_tests": false,
"include_in_public_examples": true
},
{
"title": "Create Table from COCO Keypoints",
"blurb": "Create a 3LC keypoints table from COCO-format annotations for human pose estimation and keypoint detection tasks.",
"path": "1-create-tables/keypoints/create-table-from-coco-keypoints.ipynb",
"thumbnail": "images/coco-keypoints.png",
"tags": [
"keypoints",
"coco"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - COCO128",
"dataset_name": "COCO128",
"run_name": null,
"table_name": "initial-keypoints",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20COCO128/datasets/COCO128/tables/initial-keypoints"
},
"include_in_docs": true,
"include_in_tests": true,
"include_in_public_examples": true
},
{
"title": "Create Table from YOLO Keypoints",
"blurb": "Create a 3LC keypoints table from YOLO-format dataset with normalized keypoint coordinates for efficient pose estimation workflows.",
"path": "1-create-tables/keypoints/create-table-from-yolo-keypoints.ipynb",
"thumbnail": "images/create-yolo-table.png",
"tags": [
"keypoints",
"yolo",
"pose-estimation",
"beginner"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": false,
"include_in_tests": false,
"include_in_public_examples": false
},
{
"title": "Create Custom Oriented Bounding Box Table",
"blurb": "Create a 3LC Table with oriented bounding boxes using the HRSC2016-MS maritime ship detection dataset for rotated object detection.",
"path": "1-create-tables/obbs/create-custom-obb-table.ipynb",
"thumbnail": "images/hrsc2016-ms.png",
"tags": [
"oriented-bounding-boxes",
"maritime"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - OBBs",
"dataset_name": "HRSC2016-MS",
"run_name": null,
"table_name": null,
"object_url": null
},
"include_in_docs": true,
"include_in_tests": false,
"include_in_public_examples": true
},
{
"title": "Create Table from YOLO - Oriented Bounding Boxes (OBB)",
"blurb": "This notebook demonstrates how to create a 3LC Table from a YOLO-format dataset for **oriented bounding box detection** tasks.",
"path": "1-create-tables/obbs/create-table-from-yolo-obb.ipynb",
"thumbnail": "",
"tags": [],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": false,
"include_in_tests": false,
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},
{
"title": "Create Custom Bounding Box Table",
"blurb": "Build a 3LC Table from scratch with custom bounding box annotations and schema definitions for specialized object detection scenarios.",
"path": "1-create-tables/object detection/create-custom-bb-table.ipynb",
"thumbnail": "images/create-bb-table.jpg",
"tags": [
"object-detection"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Cats & Dogs",
"dataset_name": null,
"run_name": null,
"table_name": null,
"object_url": null
},
"include_in_docs": true,
"include_in_tests": true,
"include_in_public_examples": true
},
{
"title": "Create Table from COCO Object Detection",
"blurb": "Create a 3LC Table from COCO-format dataset containing images with bounding box annotations for standard object detection tasks.",
"path": "1-create-tables/object detection/create-table-from-coco-detection.ipynb",
"thumbnail": "images/coco.jpg",
"tags": [
"object-detection",
"coco"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - COCO128",
"dataset_name": "COCO128",
"run_name": null,
"table_name": "initial",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20COCO128/datasets/COCO128/tables/initial"
},
"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Create Table from YOLO Object Detection",
"blurb": "Create a 3LC Table from YOLO-format dataset with normalized bounding box annotations for efficient object detection workflows.",
"path": "1-create-tables/object detection/create-table-from-yolo-detection.ipynb",
"thumbnail": "images/create-yolo-table.png",
"tags": [
"object-detection",
"yolo",
"bounding-boxes",
"beginner"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": false,
"include_in_tests": false,
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},
{
"title": "Weighted Table Subset Selection",
"blurb": "This notebook demonstrates how to apply zero weights to a subset of table rows for selective data processing.",
"path": "2-modify-tables/1-weight-coreset.ipynb",
"thumbnail": "images/weight-coreset.png",
"tags": [
"data-curation"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Add uniqueness metrics to Table",
"blurb": "This notebook computes the global image metrics \"uniqueness\" and \"diversity\" for each image in a dataset containing images and labels.",
"path": "2-modify-tables/add-classification-metrics.ipynb",
"thumbnail": "images/add-classification-metrics.png",
"tags": [
"classification",
"metrics",
"cifar-10",
"data-curation"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Add image embeddings to a Table",
"blurb": "In this example we will extend an existing table with embeddings computed from a pre-trained model.",
"path": "2-modify-tables/add-embeddings.ipynb",
"thumbnail": "images/add-embeddings.jpg",
"tags": [
"embeddings",
"dimensionality-reduction",
"coco"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Add image metrics to a Table",
"blurb": "In this example we will extend a Table containing images with new columns containing common image metrics (sharpness, contrast, etc.).",
"path": "2-modify-tables/add-image-metrics.ipynb",
"thumbnail": "images/add-image-metrics.jpg",
"tags": [
"coco",
"metrics",
"data-curation"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": true,
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},
{
"title": "Train a instance classifier on a 3LC Table",
"blurb": "In this tutorial, we will fine-tune a classifier using instances (segmentations or bounding boxes) from a 3LC `Table`.",
"path": "2-modify-tables/add-instance-embeddings/1-train-crop-model.ipynb",
"thumbnail": "images/instance-embeddings.png",
"tags": [
"object-detection",
"classification",
"embeddings",
"metrics"
],
"parameters_cell_source": "",
"project_meta": {
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"table_name": null,
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"include_in_docs": true,
"include_in_tests": true,
"include_in_public_examples": true
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{
"title": "Collect and reduce classifier embeddings",
"blurb": "In this tutorial, we will use an existing classifier model to generate per-instance embeddings for a COCO-style object detection dataset. We will then reduce these embeddings to 3D using PaCMAP.",
"path": "2-modify-tables/add-instance-embeddings/2-add-instance-metrics.ipynb",
"thumbnail": "images/instance-embeddings.png",
"tags": [
"classification",
"object-detection",
"embeddings"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - COCO128",
"dataset_name": null,
"run_name": null,
"table_name": null,
"object_url": null
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"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Add new data to dataset with splits",
"blurb": "Add new data and re-split a dataset.",
"path": "2-modify-tables/add-new-data-and-split.ipynb",
"thumbnail": "images/add-new-data-and-split.png",
"tags": [
"table-lineage"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": true,
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},
{
"title": "Add new data to existing Table lineage",
"blurb": "Adding new data to an existing dataset is a common task, as more data is collected and we want to leverage it to improve the model. This notebook demonstrates how to add new data to an existing 3LC...",
"path": "2-modify-tables/add-new-data.ipynb",
"thumbnail": "",
"tags": [
"table-lineage"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Cats & Dogs",
"dataset_name": "cats-and-dogs",
"run_name": null,
"table_name": null,
"object_url": null
},
"include_in_docs": true,
"include_in_tests": true,
"include_in_public_examples": true
},
{
"title": "Auto-segment images using SAM",
"blurb": "This notebook creates a 3LC Table with auto-generated segmentation masks from SAM using grid-based point prompting.",
"path": "2-modify-tables/autosegment-image-column.ipynb",
"thumbnail": "images/sam-autosegment.jpg",
"tags": [
"sam",
"instance-segmentation",
"auto-labeling"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - COCO128",
"dataset_name": "AutoSegmented Images",
"run_name": null,
"table_name": "autosegmented_images",
"object_url": "https://dashboard.3lc.ai?tables=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20COCO128/datasets/AutoSegmented%20Images/tables/autosegmented_images"
},
"include_in_docs": true,
"include_in_tests": false,
"include_in_public_examples": true
},
{
"title": "Per Bounding Box Luminosity Calculation",
"blurb": "This notebook demonstrates how to calculate the luminosity of images and their bounding boxes and add them as columns to a Table.",
"path": "2-modify-tables/compute-per-bb-metrics.ipynb",
"thumbnail": "images/compute-per-bb-metrics.png",
"tags": [
"metrics",
"object-detection",
"coco"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - COCO128",
"dataset_name": "COCO128",
"run_name": null,
"table_name": null,
"object_url": null
},
"include_in_docs": true,
"include_in_tests": true,
"include_in_public_examples": true
},
{
"title": "Compare dimensionality reduction methods",
"blurb": "This notebook demonstrates how to perform dimensionality reduction on a column in a `tlc.Table` using two different dimensionality reduction algorithms, `pacmap` and `umap`.",
"path": "2-modify-tables/dimensionality-reduction-toy-example.ipynb",
"thumbnail": "images/reduce-mammoth.png",
"tags": [
"dimensionality-reduction"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Split tables",
"blurb": "Datasets are commonly divided into splits for training, validation and testing. This notebook shows how a single Table can be divided into two or more such splits, with different strategies for how...",
"path": "2-modify-tables/split-tables.ipynb",
"thumbnail": "images/split-tables.png",
"tags": [
"table-lineage"
],
"parameters_cell_source": "",
"project_meta": {},
"include_in_docs": true,
"include_in_tests": true,
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{
"title": "Convert bounding boxes to instance segmentation masks using SAM",
"blurb": "This notebook creates a derived Table with an added column containing instance segmentation masks generated by the SAM model using the Table's existing bounding boxes as prompts.",
"path": "2-modify-tables/transform-bbs-to-segs.ipynb",
"thumbnail": "images/bb2seg.jpg",
"tags": [
"sam",
"instance-segmentation",
"object-detection",
"coco"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - COCO128",
"dataset_name": null,
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"table_name": null,
"object_url": null
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"include_in_docs": true,
"include_in_tests": false,
"include_in_public_examples": true
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{
"title": "Fine-tune a object detection model using Detectron2",
"blurb": "This notebook shows how to collect bounding box metrics while training a model using Detectron2.",
"path": "3-training-and-metrics/detectron2-balloons-detection-finetuning.ipynb",
"thumbnail": "images/balloons-det-d2.png",
"tags": [
"detectron2",
"object-detection",
"balloons",
"training"
],
"parameters_cell_source": "",
"project_meta": {
"project_name": "3LC Tutorials - Balloons",
"dataset_name": null,
"run_name": "Fine-tune balloon detector",
"table_name": null,
"object_url": "https://dashboard.3lc.ai?runs=s3%3A//3lc-public-examples-2-2-dev/projects/3LC%20Tutorials%20-%20Balloons/runs/Fine-tune%20balloon%20detector"
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"include_in_docs": true,
"include_in_tests": false,
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{
"title": "Fine-tune a instance segmentation model using Detectron2",
"blurb": "This notebook shows how to collect instance segmentation metrics while training a model using Detectron2.",
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