index.yaml

examples:
  - title: "quickstart"
    path: "examples/quickstart.ipynb"
    description: "A quickstart example for getting your feet wet with FiftyOne"
  - title: "walkthrough"
    path: "examples/walkthrough.ipynb"
    description: "A more in-depth alternative to the quickstart that covers the basics of FiftyOne"
  - title: "ai_telephone"
    path: "examples/ai_telephone.ipynb"
    description: "Play multimodal AI telephone with text-to-image models, image-to-text models, and Fiftyone"
  - title: "clean_conceptual_captions"
    path: "examples/clean_conceptual_captions.ipynb"
    description: "Clean Google's Conceptual Captions Dataset with Fiftyone to train your own ControlNet"
  - title: "comparing_YOLO_and_EfficientDet"
    path: "examples/comparing_YOLO_and_EfficientDet.ipynb"
    description: "Compares the YOLOv4 and EfficientDet object detection models on the COCO dataset"
  - title: "digging_into_coco"
    path: "examples/digging_into_coco.ipynb"
    description: "A simple example of how to find mistakes in your detection datasets"
  - title: "deepfakes_in_politics"
    path: "examples/deepfakes_in_politics.ipynb"
    description: "Evaluating deepfakes using a deepfake detection algorithm and visualizing the results in FiftyOne"
  - title: "emotion_recognition_presidential_debate"
    path: "examples/emotion_recognition_presidential_debate.ipynb"
    description: "Analyzing the 2020 US Presidential Debates using an emotion recognition model"
  - title: "image_uniqueness"
    path: "examples/image_uniqueness.ipynb"
    description: "Using FiftyOne's image uniqueness method to analyze and extract insights from unlabeled datasets"
  - title: "structured_noise_injection"
    path: "examples/structured_noise_injection.ipynb"
    description: "Visually exploring a method for structured noise injection in GANs from CVPR 2020"
  - title: "visym_pip_175k"
    path: "examples/visym_pip_175k.ipynb"
    description: "Exploring the People in Public 175K Dataset from Visym Labs with FiftyOne"
  - title: "wrangling_datasets"
    path: "examples/wrangling_datasets.ipynb"
    description: "Using FiftyOne to load, manipulate, and export datasets in common formats"
  - title: "open_images_evaluation"
    path: "examples/open_images_evaluation/open_images_evaluation.ipynb"
    description: "Evaluating the quality of the ground truth annotations of the Open Images Dataset with FiftyOne"
  - title: "working_with_feature_points"
    path: "examples/working_with_feature_points.ipynb"
    description: "A simple example of computing feature points for images and visualizing them in FiftyOne"
  - title: "image_deduplication"
    path: "examples/image_deduplication.ipynb"
    description: "Find and remove duplicate images in your image datasets with FiftyOne"
  - title: "hardness_for_image_classification"
    path: "examples/exploring_classification_hardness.ipynb"
    description: "Use the FiftyOne Brain to mine the hardest images in your classification dataset"
  - title: "pytorch_detection_training"
    path: "examples/pytorch_detection_training.ipynb"
    description: "Using FiftyOne datasets to train a PyTorch object detection model"
  - title: "pytorchvideo_model_evaluation"
    path: "examples/pytorchvideo_tutorial.ipynb"
    description: "Evaluate and visualize PyTorchVideo models with FiftyOne"
  - title: "training_clearml_detector"
    path: "examples/training_clearml_detector.ipynb"
    description: "Train a model with ClearML and FiftyOne to detect DRAGONS!"
  - title: "converting_tags_to_classifications"
    path: "examples/convert_tags_to_classifications.ipynb"
    description: "Convert classifications to tags and back to annotate them right in the FiftyOne App"
  - title: "Qdrant_FiftyOne_Recipe"
    path: "examples/Qdrant_FiftyOne_Recipe.ipynb"
    description: "Nearest neighbor classification of embeddings with Qdrant"
  - title: "armbench_defect_detection"
    path: "examples/armbench_defect_detection.ipynb"
    description: "Visualizing Defects in Amazon’s ARMBench Dataset Using Embeddings and OpenAI’s CLIP Model"
  - title: "openvino_model_horizontal_text_detection"
    path: "examples/openvino_detection_with_fiftyone.ipynb"
    description: "Horizontal text detection on Total-Text Dataset using OpenVino Model"