Click this link to **run** the notebook in
[Google Colab](https://colab.research.google.com) (no setup required!)
Click this link to **view** the notebook in
[Jupyter nbviewer](https://nbviewer.jupyter.org)
Click this link to **download** the notebook
### Running examples locally
You can always clone this repository:
```shell
git clone https://github.com/voxel51/fiftyone-examples
```
and run any example locally. Make sure you have
[Jupyter installed](https://jupyter.org/install) and then run:
```shell
jupyter notebook examples/an_awesome_example.ipynb
```
## Table of contents
| Shortcuts | Examples | Description |
|---|---|---|
|
|
quickstart | A quickstart example for getting your feet wet with FiftyOne |
|
|
walkthrough | A more in-depth alternative to the quickstart that covers the basics of FiftyOne |
|
|
ai_telephone | Play multimodal AI telephone with text-to-image models, image-to-text models, and Fiftyone |
|
|
clean_conceptual_captions | Clean Google's Conceptual Captions Dataset with Fiftyone to train your own ControlNet |
|
|
segment_anything_openvino | Add object masks to a FiftyOne dataset with OpenVINO-optimized Segment Anything Model |
|
|
comparing_YOLO_and_EfficientDet | Compares the YOLOv4 and EfficientDet object detection models on the COCO dataset |
|
|
digging_into_coco | A simple example of how to find mistakes in your detection datasets |
|
|
deepfakes_in_politics | Evaluating deepfakes using a deepfake detection algorithm and visualizing the results in FiftyOne |
|
|
emotion_recognition_presidential_debate | Analyzing the 2020 US Presidential Debates using an emotion recognition model |
|
|
image_uniqueness | Using FiftyOne's image uniqueness method to analyze and extract insights from unlabeled datasets |
|
|
structured_noise_injection | Visually exploring a method for structured noise injection in GANs from CVPR 2020 |
|
|
visym_pip_175k | Exploring the People in Public 175K Dataset from Visym Labs with FiftyOne |
|
|
wrangling_datasets | Using FiftyOne to load, manipulate, and export datasets in common formats |
|
|
open_images_evaluation | Evaluating the quality of the ground truth annotations of the Open Images Dataset with FiftyOne |
|
|
working_with_feature_points | A simple example of computing feature points for images and visualizing them in FiftyOne |
|
|
image_deduplication | Find and remove duplicate images in your image datasets with FiftyOne |
|
|
hardness_for_image_classification | Use the FiftyOne Brain to mine the hardest images in your classification dataset |
|
|
pytorch_detection_training | Using FiftyOne datasets to train a PyTorch object detection model |
|
|
pytorchvideo_model_evaluation | Evaluate and visualize PyTorchVideo models with FiftyOne |
|
|
training_clearml_detector | Train a model with ClearML and FiftyOne to detect DRAGONS! |
|
|
converting_tags_to_classifications | Convert classifications to tags and back to annotate them right in the FiftyOne App |
|
|
Qdrant_FiftyOne_Recipe | Nearest neighbor classification of embeddings with Qdrant |
|
|
armbench_defect_detection | Visualizing Defects in Amazon’s ARMBench Dataset Using Embeddings and OpenAI’s CLIP Model |
|
|
openvino_model_horizontal_text_detection | Horizontal text detection on Total-Text Dataset using OpenVino Model |
|
|
chest_xray14 | Load and explore the NIH's ChestX-ray14 dataset in FiftyOne |
|
|
football_player_segmentation | Detection and Segmentation on Football Player Segmentation Dataset using SAM |
|
|
wildme_conservation_datasets | Create a 'meta' dataset out of three WildMe conservation datasets in FiftyOne |
|
|
CLI Tips & Tricks | Use FiftyOne's Command Line Interface to expedite your workflows |
|
|
Grouped Dataset Tips & Tricks | Learn how to work with grouped datasets in FiftyOne |