{ "cells": [ { "cell_type": "markdown", "id": "2ecf2e51", "metadata": {}, "source": [ "# Exploring the Model Zoo\n", "\n", "This experience introduces you to the core components of the FiftyOne Zoo:\n", "- The **Dataset Zoo** for accessing and exploring public datasets\n", "- The **Model Zoo** for running pre-trained models on your data\n", "- Creating your **own remotely-sourced datasets** for reuse and collaboration\n", "\n", "Whether you're a researcher, engineer, or educator, these tools help streamline your computer vision workflows in FiftyOne.\n", "\n", "> 💡 Make sure to run `pip install fiftyone torch torchvision` before starting." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#!pip install fiftyone\n", "#!pip install torch torchvision" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FiftyOne Zoo: A Hub for Datasets and Models\n", "\n", "FiftyOne Zoo provides easy access to a vast collection of pre-built datasets and pre-trained models. This notebook will guide you through exploring and using these resources.\n", "\n", "### Key Components:\n", "\n", "* **Dataset Zoo:** Offers a wide range of computer vision datasets, ready for immediate use.\n", "* **Model Zoo:** Provides pre-trained models for various tasks, enabling quick experimentation and deployment.\n", "\n", "Let's dive in!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import fiftyone as fo\n", "import fiftyone.zoo as foz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Zoo\n", "\n", "## Exploring the Model Zoo\n", "\n", "The Model Zoo provides pre-trained models that can be used for inference and evaluation.\n", "\n", "### Listing Available Models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"\\nAvailable Models:\")\n", "for model_name in foz.list_zoo_models():\n", " print(f\"- {model_name}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "FiftyOne supports a wide range of models for classification, detection, and segmentation, please visit the [Model Zoo](https://docs.voxel51.com/model_zoo/models.html) for the full, up-to-date catalog.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading a Model (Example: AlexNet on ImageNet)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading model from 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth'...\n", " 100% |██████| 1.8Gb/1.8Gb [3.7s elapsed, 0s remaining, 527.5Mb/s] \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /home/paula/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n", "100.0%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model = foz.load_zoo_model(\"alexnet-imagenet-torch\")\n", "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![imagenet](https://cdn.voxel51.com/getting_started_model_dataset_zoo/notebook2/imagenet.webp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Applying a Model to a Dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset already downloaded\n", "Loading existing dataset 'imagenet-sample'. To reload from disk, either delete the existing dataset or provide a custom `dataset_name` to use\n", " 100% |███████████████| 1000/1000 [4.6s elapsed, 0s remaining, 277.2 samples/s] \n", "None\n", "Session launched. Run `session.show()` to open the App in a cell output.\n" ] } ], "source": [ "try:\n", " dataset = foz.load_zoo_dataset(\"imagenet-sample\")\n", " predictions = dataset.apply_model(model, label_field=\"predictions\")\n", " print(predictions)\n", "except:\n", " print(\"imagenet-sample dataset is not available, please install it if needed.\")\n", "\n", "session = fo.launch_app(dataset, port=5152, auto=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluating Model Performance\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading voxel51/fiftyone-plugins...\n", " 329.3Mb [12.2s elapsed, ? remaining, 32.9Mb/s] \n", "Skipping existing plugin '@voxel51/evaluation'\n" ] } ], "source": [ "!fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-names @voxel51/evaluation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Afghan hound 1.00 1.00 1.00 1\n", " African chameleon 0.33 1.00 0.50 1\n", " African crocodile 1.00 1.00 1.00 1\n", " African elephant 0.00 0.00 0.00 0\n", " African grey 1.00 1.00 1.00 1\n", " African hunting dog 1.00 1.00 1.00 1\n", " Airedale 1.00 1.00 1.00 1\n", "American Staffordshire terrier 0.00 0.00 0.00 1\n", " American alligator 0.00 0.00 0.00 1\n", " American black bear 0.00 0.00 0.00 1\n", " American chameleon 0.00 0.00 0.00 1\n", " American coot 0.00 0.00 0.00 1\n", " American egret 0.00 0.00 0.00 1\n", " American lobster 1.00 1.00 1.00 1\n", " Angora 0.33 1.00 0.50 1\n", " Appenzeller 0.00 0.00 0.00 1\n", " Arabian camel 0.25 1.00 0.40 1\n", " Arctic fox 0.50 1.00 0.67 1\n", " Australian terrier 0.00 0.00 0.00 1\n", " Band Aid 0.00 0.00 0.00 1\n", " Bedlington terrier 1.00 1.00 1.00 1\n", " Bernese mountain dog 0.00 0.00 0.00 1\n", " Blenheim spaniel 1.00 1.00 1.00 1\n", " Border collie 0.00 0.00 0.00 1\n", " Border terrier 0.00 0.00 0.00 1\n", " Boston bull 1.00 1.00 1.00 1\n", " Bouvier des Flandres 0.00 0.00 0.00 1\n", " Brabancon griffon 1.00 1.00 1.00 1\n", " Brittany spaniel 1.00 1.00 1.00 1\n", " CD player 0.00 0.00 0.00 0\n", " Cardigan 0.00 0.00 0.00 2\n", " Chesapeake Bay retriever 0.00 0.00 0.00 1\n", " Chihuahua 0.00 0.00 0.00 1\n", " Dandie Dinmont 0.00 0.00 0.00 1\n", " Doberman 0.50 1.00 0.67 1\n", " Dutch oven 0.00 0.00 0.00 1\n", " Egyptian cat 0.00 0.00 0.00 0\n", " English setter 0.00 0.00 0.00 1\n", " English springer 0.00 0.00 0.00 1\n", " EntleBucher 1.00 1.00 1.00 1\n", " Eskimo dog 0.00 0.00 0.00 2\n", " European fire salamander 1.00 1.00 1.00 1\n", " European gallinule 0.00 0.00 0.00 1\n", " French bulldog 1.00 1.00 1.00 1\n", " French horn 1.00 1.00 1.00 1\n", " German shepherd 1.00 1.00 1.00 1\n", " German short-haired pointer 1.00 1.00 1.00 1\n", " Gila monster 1.00 1.00 1.00 1\n", " Gordon setter 0.00 0.00 0.00 1\n", " Granny Smith 1.00 1.00 1.00 1\n", " Great Dane 0.00 0.00 0.00 1\n", " Great Pyrenees 1.00 0.33 0.50 3\n", " Ibizan hound 0.00 0.00 0.00 1\n", " Indian cobra 1.00 1.00 1.00 1\n", " Indian elephant 0.00 0.00 0.00 0\n", " Irish setter 1.00 1.00 1.00 1\n", " Irish terrier 1.00 1.00 1.00 1\n", " Irish water spaniel 0.50 1.00 0.67 1\n", " Irish wolfhound 0.00 0.00 0.00 1\n", " Italian greyhound 1.00 1.00 1.00 2\n", " Japanese spaniel 1.00 1.00 1.00 1\n", " Kerry blue terrier 0.50 1.00 0.67 1\n", " Komodo dragon 0.50 1.00 0.67 1\n", " Lakeland terrier 1.00 1.00 1.00 1\n", " Leonberg 1.00 1.00 1.00 1\n", " Lhasa 1.00 1.00 1.00 1\n", " Loafer 1.00 1.00 1.00 1\n", " Madagascar cat 0.50 1.00 0.67 1\n", " Maltese dog 0.50 1.00 0.67 1\n", " Mexican hairless 0.00 0.00 0.00 0\n", " Model T 0.50 1.00 0.67 1\n", " Newfoundland 1.00 1.00 1.00 1\n", " Norfolk terrier 1.00 1.00 1.00 1\n", " Norwegian elkhound 1.00 1.00 1.00 1\n", " Norwich terrier 0.00 0.00 0.00 1\n", " Old English sheepdog 0.00 0.00 0.00 1\n", " Pekinese 1.00 1.00 1.00 1\n", " Pembroke 0.00 0.00 0.00 0\n", " Persian cat 1.00 1.00 1.00 1\n", " Petri dish 0.50 1.00 0.67 1\n", " Polaroid camera 0.50 0.50 0.50 2\n", " Pomeranian 1.00 1.00 1.00 1\n", " Rhodesian ridgeback 0.00 0.00 0.00 1\n", " Rottweiler 0.00 0.00 0.00 2\n", " Saint Bernard 1.00 1.00 1.00 1\n", " Saluki 0.50 0.50 0.50 2\n", " Samoyed 0.50 1.00 0.67 1\n", " Scotch terrier 1.00 1.00 1.00 1\n", " Scottish deerhound 0.50 1.00 0.67 1\n", " Sealyham terrier 0.00 0.00 0.00 1\n", " Shetland sheepdog 0.00 0.00 0.00 1\n", " Shih-Tzu 0.50 0.50 0.50 2\n", " Siamese cat 0.50 1.00 0.67 1\n", " Siberian husky 0.33 1.00 0.50 1\n", " Staffordshire bullterrier 1.00 1.00 1.00 1\n", " Sussex spaniel 0.00 0.00 0.00 1\n", " Tibetan terrier 0.00 0.00 0.00 1\n", " Walker hound 0.00 0.00 0.00 2\n", " Weimaraner 0.50 1.00 0.67 1\n", " Welsh springer spaniel 0.50 1.00 0.67 1\n", " West Highland white terrier 1.00 1.00 1.00 1\n", " Yorkshire terrier 0.00 0.00 0.00 1\n", " abaya 0.00 0.00 0.00 0\n", " accordion 0.00 0.00 0.00 1\n", " acorn 1.00 1.00 1.00 1\n", " acorn squash 0.50 1.00 0.67 1\n", " acoustic guitar 0.00 0.00 0.00 1\n", " admiral 0.00 0.00 0.00 1\n", " affenpinscher 1.00 1.00 1.00 1\n", " agama 0.00 0.00 0.00 0\n", " agaric 1.00 1.00 1.00 2\n", " aircraft carrier 0.00 0.00 0.00 2\n", " airliner 1.00 1.00 1.00 1\n", " airship 0.00 0.00 0.00 2\n", " albatross 0.00 0.00 0.00 1\n", " alligator lizard 0.00 0.00 0.00 1\n", " altar 0.00 0.00 0.00 1\n", " ambulance 0.50 1.00 0.67 1\n", " anemone fish 1.00 0.50 0.67 2\n", " ant 0.00 0.00 0.00 1\n", " apiary 0.50 1.00 0.67 1\n", " apron 1.00 1.00 1.00 1\n", " armadillo 1.00 1.00 1.00 1\n", " artichoke 0.00 0.00 0.00 0\n", " ashcan 0.00 0.00 0.00 1\n", " axolotl 0.50 1.00 0.67 1\n", " baboon 0.00 0.00 0.00 1\n", " backpack 0.00 0.00 0.00 0\n", " bakery 1.00 1.00 1.00 1\n", " balance beam 0.00 0.00 0.00 1\n", " bald eagle 0.33 0.50 0.40 2\n", " balloon 1.00 1.00 1.00 1\n", " ballplayer 0.00 0.00 0.00 1\n", " ballpoint 0.50 1.00 0.67 1\n", " banana 0.00 0.00 0.00 0\n", " banded gecko 0.00 0.00 0.00 1\n", " banjo 1.00 1.00 1.00 1\n", " bannister 0.00 0.00 0.00 0\n", " barbell 1.00 1.00 1.00 1\n", " barber chair 0.50 1.00 0.67 1\n", " barbershop 0.00 0.00 0.00 1\n", " barn 0.50 1.00 0.67 1\n", " barn spider 1.00 1.00 1.00 1\n", " barometer 1.00 1.00 1.00 1\n", " barracouta 1.00 1.00 1.00 1\n", " barrel 1.00 1.00 1.00 1\n", " barrow 0.00 0.00 0.00 1\n", " baseball 1.00 1.00 1.00 1\n", " basenji 0.00 0.00 0.00 1\n", " basketball 1.00 1.00 1.00 1\n", " basset 0.00 0.00 0.00 2\n", " bassinet 0.00 0.00 0.00 1\n", " bassoon 0.00 0.00 0.00 1\n", " bath towel 0.00 0.00 0.00 0\n", " bathing cap 0.00 0.00 0.00 1\n", " bathtub 0.00 0.00 0.00 2\n", " beach wagon 0.50 0.50 0.50 2\n", " beacon 0.00 0.00 0.00 1\n", " beagle 0.67 0.67 0.67 3\n", " beaker 0.00 0.00 0.00 1\n", " bearskin 1.00 1.00 1.00 1\n", " beaver 0.50 1.00 0.67 1\n", " bee 1.00 0.50 0.67 2\n", " bee eater 0.00 0.00 0.00 0\n", " beer bottle 1.00 1.00 1.00 1\n", " beer glass 1.00 1.00 1.00 1\n", " bell cote 0.00 0.00 0.00 1\n", " bell pepper 1.00 1.00 1.00 1\n", " bib 0.00 0.00 0.00 1\n", " bicycle-built-for-two 1.00 0.50 0.67 2\n", " bighorn 1.00 1.00 1.00 1\n", " bikini 1.00 1.00 1.00 1\n", " binder 0.50 1.00 0.67 1\n", " binoculars 0.00 0.00 0.00 1\n", " birdhouse 1.00 1.00 1.00 1\n", " bison 0.50 1.00 0.67 1\n", " bittern 0.00 0.00 0.00 1\n", " black and gold garden spider 1.00 1.00 1.00 1\n", " black grouse 1.00 1.00 1.00 1\n", " black stork 0.00 0.00 0.00 1\n", " black swan 0.50 1.00 0.67 1\n", " black widow 1.00 1.00 1.00 1\n", " black-footed ferret 1.00 1.00 1.00 1\n", " bluetick 0.33 1.00 0.50 1\n", " boa constrictor 0.00 0.00 0.00 1\n", " boathouse 0.25 1.00 0.40 1\n", " bobsled 0.00 0.00 0.00 2\n", " bolete 1.00 1.00 1.00 1\n", " bolo tie 0.00 0.00 0.00 1\n", " bonnet 0.00 0.00 0.00 2\n", " book jacket 0.00 0.00 0.00 1\n", " bookcase 1.00 1.00 1.00 1\n", " bookshop 0.00 0.00 0.00 1\n", " borzoi 0.00 0.00 0.00 1\n", " bottlecap 1.00 1.00 1.00 1\n", " bow 1.00 1.00 1.00 1\n", " bow tie 0.00 0.00 0.00 0\n", " boxer 0.50 1.00 0.67 1\n", " brain coral 1.00 1.00 1.00 1\n", " brambling 0.00 0.00 0.00 1\n", " brass 0.33 1.00 0.50 1\n", " brassiere 0.00 0.00 0.00 0\n", " breakwater 0.00 0.00 0.00 2\n", " breastplate 0.00 0.00 0.00 2\n", " briard 0.50 0.50 0.50 2\n", " broccoli 1.00 1.00 1.00 1\n", " broom 0.50 1.00 0.67 2\n", " brown bear 1.00 1.00 1.00 1\n", " bubble 1.00 1.00 1.00 1\n", " bucket 0.00 0.00 0.00 0\n", " buckeye 1.00 1.00 1.00 1\n", " buckle 0.50 1.00 0.67 1\n", " bulbul 0.00 0.00 0.00 1\n", " bull mastiff 0.50 1.00 0.67 1\n", " bullet train 0.50 0.50 0.50 2\n", " bulletproof vest 0.50 1.00 0.67 1\n", " bullfrog 0.00 0.00 0.00 1\n", " burrito 0.50 1.00 0.67 1\n", " bustard 0.00 0.00 0.00 1\n", " butcher shop 1.00 1.00 1.00 1\n", " butternut squash 0.00 0.00 0.00 1\n", " cab 0.00 0.00 0.00 2\n", " cabbage butterfly 0.50 1.00 0.67 1\n", " cairn 0.50 1.00 0.67 1\n", " caldron 0.00 0.00 0.00 1\n", " can opener 1.00 1.00 1.00 1\n", " candle 0.00 0.00 0.00 0\n", " cannon 0.00 0.00 0.00 0\n", " canoe 1.00 1.00 1.00 1\n", " capuchin 0.00 0.00 0.00 1\n", " car mirror 1.00 1.00 1.00 1\n", " car wheel 0.00 0.00 0.00 1\n", " carbonara 0.00 0.00 0.00 1\n", " cardigan 1.00 0.50 0.67 2\n", " cardoon 1.00 0.50 0.67 2\n", " carousel 0.00 0.00 0.00 1\n", " carpenter's kit 0.00 0.00 0.00 1\n", " carton 0.00 0.00 0.00 0\n", " cash machine 0.50 1.00 0.67 1\n", " cassette 1.00 1.00 1.00 1\n", " cassette player 0.00 0.00 0.00 1\n", " castle 1.00 1.00 1.00 1\n", " catamaran 1.00 1.00 1.00 1\n", " cauliflower 1.00 1.00 1.00 1\n", " cello 1.00 1.00 1.00 1\n", " cellular telephone 0.50 0.50 0.50 2\n", " chain 1.00 1.00 1.00 1\n", " chain mail 0.50 1.00 0.67 1\n", " chain saw 0.00 0.00 0.00 1\n", " chainlink fence 1.00 0.50 0.67 2\n", " chambered nautilus 1.00 0.50 0.67 2\n", " cheeseburger 1.00 1.00 1.00 1\n", " cheetah 1.00 1.00 1.00 2\n", " chest 0.00 0.00 0.00 0\n", " chickadee 0.00 0.00 0.00 1\n", " chiffonier 0.00 0.00 0.00 1\n", " chime 0.00 0.00 0.00 2\n", " chimpanzee 1.00 1.00 1.00 1\n", " china cabinet 1.00 0.50 0.67 2\n", " chiton 0.50 1.00 0.67 1\n", " chow 1.00 1.00 1.00 1\n", " church 0.50 1.00 0.67 1\n", " cicada 1.00 1.00 1.00 1\n", " cinema 1.00 1.00 1.00 1\n", " cleaver 0.00 0.00 0.00 1\n", " cliff dwelling 1.00 1.00 1.00 1\n", " cloak 0.00 0.00 0.00 0\n", " clog 0.00 0.00 0.00 0\n", " clumber 1.00 1.00 1.00 1\n", " cocker spaniel 0.00 0.00 0.00 1\n", " cockroach 0.50 1.00 0.67 1\n", " coffeepot 0.00 0.00 0.00 1\n", " coho 0.50 1.00 0.67 1\n", " coil 0.00 0.00 0.00 0\n", " collie 0.00 0.00 0.00 1\n", " comic book 0.00 0.00 0.00 0\n", " common iguana 1.00 1.00 1.00 1\n", " common newt 0.00 0.00 0.00 1\n", " computer keyboard 0.00 0.00 0.00 1\n", " conch 0.50 1.00 0.67 1\n", " confectionery 1.00 1.00 1.00 1\n", " consomme 0.00 0.00 0.00 1\n", " container ship 1.00 1.00 1.00 1\n", " convertible 0.00 0.00 0.00 1\n", " coral fungus 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0.00 0.00 0.00 1\n", " dial telephone 1.00 1.00 1.00 1\n", " diamondback 0.00 0.00 0.00 1\n", " diaper 0.33 1.00 0.50 1\n", " digital clock 0.00 0.00 0.00 1\n", " digital watch 1.00 0.50 0.67 2\n", " dining table 1.00 1.00 1.00 1\n", " dishrag 0.50 1.00 0.67 1\n", " dishwasher 0.67 1.00 0.80 2\n", " disk brake 1.00 1.00 1.00 1\n", " dock 0.00 0.00 0.00 0\n", " dogsled 1.00 1.00 1.00 1\n", " dome 0.00 0.00 0.00 2\n", " dough 1.00 1.00 1.00 1\n", " dowitcher 0.33 1.00 0.50 1\n", " dragonfly 0.00 0.00 0.00 1\n", " drake 1.00 1.00 1.00 1\n", " drilling platform 0.00 0.00 0.00 0\n", " drum 0.00 0.00 0.00 1\n", " drumstick 0.00 0.00 0.00 1\n", " dumbbell 1.00 0.50 0.67 2\n", " dung beetle 1.00 1.00 1.00 1\n", " ear 1.00 1.00 1.00 1\n", " earthstar 1.00 1.00 1.00 1\n", " echidna 0.50 1.00 0.67 1\n", " eel 0.50 1.00 0.67 1\n", " eft 0.50 1.00 0.67 1\n", " eggnog 0.00 0.00 0.00 1\n", " electric fan 0.00 0.00 0.00 1\n", " electric guitar 1.00 1.00 1.00 1\n", " electric locomotive 1.00 1.00 1.00 1\n", " electric ray 0.50 1.00 0.67 1\n", " entertainment center 1.00 0.50 0.67 2\n", " envelope 0.00 0.00 0.00 0\n", " espresso 1.00 1.00 1.00 1\n", " face powder 0.00 0.00 0.00 1\n", " feather boa 1.00 1.00 1.00 1\n", " fiddler crab 0.00 0.00 0.00 2\n", " fig 1.00 0.50 0.67 2\n", " file 1.00 0.50 0.67 2\n", " fire engine 1.00 1.00 1.00 1\n", " fire screen 0.00 0.00 0.00 1\n", " fireboat 1.00 1.00 1.00 1\n", " flagpole 0.00 0.00 0.00 0\n", " flamingo 1.00 1.00 1.00 1\n", " flat-coated retriever 0.33 1.00 0.50 1\n", " flatworm 1.00 1.00 1.00 1\n", " flute 0.00 0.00 0.00 1\n", " football helmet 0.00 0.00 0.00 1\n", " forklift 0.00 0.00 0.00 1\n", " fountain 0.50 0.50 0.50 2\n", " fountain pen 1.00 1.00 1.00 1\n", " four-poster 0.50 1.00 0.67 1\n", " fox squirrel 0.00 0.00 0.00 0\n", " freight car 1.00 1.00 1.00 1\n", " frilled lizard 0.00 0.00 0.00 1\n", " frying pan 0.00 0.00 0.00 1\n", " fur coat 1.00 1.00 1.00 1\n", " garbage truck 1.00 1.00 1.00 1\n", " garden spider 0.00 0.00 0.00 0\n", " garter snake 0.33 0.50 0.40 2\n", " gas pump 1.00 1.00 1.00 1\n", " gasmask 0.00 0.00 0.00 1\n", " gazelle 0.00 0.00 0.00 1\n", " geyser 1.00 1.00 1.00 1\n", " giant panda 1.00 0.50 0.67 2\n", " giant schnauzer 1.00 1.00 1.00 1\n", " gibbon 1.00 0.50 0.67 2\n", " go-kart 0.00 0.00 0.00 1\n", " goblet 1.00 0.50 0.67 2\n", " goldfinch 1.00 1.00 1.00 1\n", " goldfish 0.00 0.00 0.00 1\n", " golfcart 0.00 0.00 0.00 1\n", " gondola 0.50 1.00 0.67 1\n", " gong 0.00 0.00 0.00 0\n", " goose 1.00 1.00 1.00 1\n", " gorilla 0.00 0.00 0.00 1\n", " gown 0.00 0.00 0.00 0\n", " grand piano 0.33 1.00 0.50 1\n", " grasshopper 0.33 1.00 0.50 1\n", " great grey owl 1.00 1.00 1.00 1\n", " great white shark 1.00 1.00 1.00 1\n", " green lizard 0.00 0.00 0.00 1\n", " green mamba 0.00 0.00 0.00 2\n", " green snake 0.00 0.00 0.00 0\n", " greenhouse 0.00 0.00 0.00 0\n", " grey whale 1.00 1.00 1.00 2\n", " grocery store 1.00 0.50 0.67 2\n", " groenendael 0.00 0.00 0.00 1\n", " groom 1.00 0.50 0.67 2\n", " ground beetle 0.00 0.00 0.00 1\n", " guacamole 0.00 0.00 0.00 1\n", " guillotine 1.00 0.50 0.67 2\n", " guinea pig 0.50 1.00 0.67 1\n", " gyromitra 1.00 0.50 0.67 2\n", " hair slide 0.00 0.00 0.00 2\n", " half track 0.00 0.00 0.00 1\n", " hammerhead 1.00 1.00 1.00 1\n", " hamster 0.00 0.00 0.00 2\n", " hand blower 0.50 1.00 0.67 1\n", " handkerchief 0.50 1.00 0.67 1\n", " hard disc 0.00 0.00 0.00 1\n", " hare 1.00 1.00 1.00 2\n", " harmonica 0.00 0.00 0.00 1\n", " harp 0.00 0.00 0.00 1\n", " hartebeest 0.00 0.00 0.00 0\n", " harvester 1.00 1.00 1.00 2\n", " harvestman 0.50 0.50 0.50 2\n", " hatchet 0.00 0.00 0.00 0\n", " head cabbage 0.50 1.00 0.67 1\n", " hen 1.00 1.00 1.00 2\n", " hen-of-the-woods 0.00 0.00 0.00 0\n", " hermit crab 1.00 1.00 1.00 1\n", " hip 0.50 1.00 0.67 1\n", " hippopotamus 1.00 1.00 1.00 1\n", " hognose snake 0.50 1.00 0.67 1\n", " holster 0.33 1.00 0.50 1\n", " home theater 0.50 1.00 0.67 1\n", " honeycomb 0.00 0.00 0.00 0\n", " hook 0.00 0.00 0.00 2\n", " hoopskirt 1.00 1.00 1.00 1\n", " horizontal bar 0.67 1.00 0.80 2\n", " horned viper 0.00 0.00 0.00 1\n", " horse cart 1.00 1.00 1.00 1\n", " hot pot 1.00 0.75 0.86 4\n", " hotdog 0.00 0.00 0.00 2\n", " hourglass 0.00 0.00 0.00 1\n", " house finch 1.00 1.00 1.00 1\n", " howler monkey 0.50 0.50 0.50 2\n", " hummingbird 0.50 1.00 0.67 1\n", " hyena 0.00 0.00 0.00 1\n", " iPod 0.50 1.00 0.67 1\n", " ibex 0.00 0.00 0.00 1\n", " ice bear 0.00 0.00 0.00 1\n", " ice cream 0.00 0.00 0.00 1\n", " ice lolly 0.00 0.00 0.00 1\n", " impala 1.00 1.00 1.00 1\n", " indigo bunting 0.00 0.00 0.00 1\n", " indri 1.00 1.00 1.00 1\n", " iron 0.00 0.00 0.00 0\n", " isopod 0.00 0.00 0.00 1\n", " jacamar 1.00 1.00 1.00 2\n", " jack-o'-lantern 1.00 1.00 1.00 1\n", " jackfruit 1.00 1.00 1.00 1\n", " jaguar 1.00 1.00 1.00 1\n", " jay 0.00 0.00 0.00 1\n", " jean 1.00 1.00 1.00 1\n", " jeep 1.00 1.00 1.00 1\n", " jellyfish 1.00 1.00 1.00 1\n", " jersey 1.00 1.00 1.00 1\n", " jigsaw puzzle 0.33 1.00 0.50 1\n", " jinrikisha 0.33 1.00 0.50 1\n", " joystick 0.00 0.00 0.00 1\n", " junco 1.00 1.00 1.00 1\n", " keeshond 0.50 1.00 0.67 1\n", " killer whale 1.00 1.00 1.00 1\n", " kimono 0.00 0.00 0.00 1\n", " king crab 0.50 1.00 0.67 1\n", " king penguin 1.00 1.00 1.00 1\n", " king snake 1.00 1.00 1.00 1\n", " kit fox 0.50 1.00 0.67 1\n", " kite 0.50 0.50 0.50 2\n", " knee pad 1.00 1.00 1.00 1\n", " knot 1.00 1.00 1.00 1\n", " koala 1.00 1.00 1.00 1\n", " komondor 0.50 1.00 0.67 1\n", " kuvasz 0.00 0.00 0.00 0\n", " lab coat 1.00 1.00 1.00 1\n", " lacewing 0.00 0.00 0.00 2\n", " ladle 0.00 0.00 0.00 2\n", " ladybug 0.00 0.00 0.00 1\n", " lakeside 0.00 0.00 0.00 1\n", " lampshade 0.00 0.00 0.00 1\n", " langur 0.00 0.00 0.00 1\n", " laptop 0.00 0.00 0.00 0\n", " lawn mower 1.00 1.00 1.00 1\n", " leaf beetle 0.25 1.00 0.40 1\n", " leafhopper 1.00 1.00 1.00 1\n", " lemon 0.50 1.00 0.67 1\n", " lens cap 0.00 0.00 0.00 2\n", " leopard 0.00 0.00 0.00 0\n", " lesser panda 0.00 0.00 0.00 0\n", " letter opener 0.00 0.00 0.00 2\n", " library 0.00 0.00 0.00 1\n", " lifeboat 1.00 1.00 1.00 1\n", " lighter 0.33 1.00 0.50 1\n", " limousine 1.00 1.00 1.00 1\n", " limpkin 0.00 0.00 0.00 1\n", " liner 1.00 0.67 0.80 3\n", " lion 0.50 1.00 0.67 1\n", " lionfish 0.50 1.00 0.67 1\n", " lipstick 0.33 0.50 0.40 2\n", " llama 1.00 1.00 1.00 1\n", " loggerhead 1.00 1.00 1.00 1\n", " long-horned beetle 0.00 0.00 0.00 1\n", " lorikeet 1.00 1.00 1.00 1\n", " loudspeaker 0.00 0.00 0.00 0\n", " lumbermill 1.00 1.00 1.00 1\n", " lycaenid 0.00 0.00 0.00 1\n", " lynx 0.00 0.00 0.00 2\n", " macaque 0.00 0.00 0.00 0\n", " macaw 1.00 1.00 1.00 1\n", " magnetic compass 0.00 0.00 0.00 1\n", " magpie 1.00 1.00 1.00 1\n", " mailbag 0.00 0.00 0.00 2\n", " mailbox 0.50 1.00 0.67 1\n", " maillot 0.33 1.00 0.50 1\n", " malamute 0.33 1.00 0.50 1\n", " malinois 0.67 1.00 0.80 2\n", " manhole cover 1.00 0.50 0.67 2\n", " mantis 0.00 0.00 0.00 1\n", " maraca 0.00 0.00 0.00 0\n", " marimba 0.00 0.00 0.00 1\n", " marmoset 1.00 1.00 1.00 1\n", " marmot 0.50 1.00 0.67 1\n", " mashed potato 1.00 1.00 1.00 1\n", " mask 0.50 1.00 0.67 1\n", " matchstick 0.00 0.00 0.00 1\n", " maypole 0.50 1.00 0.67 1\n", " maze 1.00 0.50 0.67 2\n", " measuring cup 1.00 1.00 1.00 1\n", " meat loaf 0.00 0.00 0.00 1\n", " medicine chest 0.00 0.00 0.00 1\n", " meerkat 1.00 1.00 1.00 1\n", " megalith 1.00 1.00 1.00 1\n", " menu 1.00 1.00 1.00 1\n", " microphone 0.50 1.00 0.67 1\n", " microwave 1.00 1.00 1.00 1\n", " military uniform 0.00 0.00 0.00 1\n", " milk can 0.00 0.00 0.00 1\n", " miniature pinscher 1.00 1.00 1.00 2\n", " miniature poodle 1.00 1.00 1.00 1\n", " miniature schnauzer 1.00 1.00 1.00 1\n", " minibus 1.00 1.00 1.00 1\n", " miniskirt 0.00 0.00 0.00 2\n", " minivan 0.00 0.00 0.00 1\n", " mink 0.00 0.00 0.00 1\n", " missile 1.00 1.00 1.00 1\n", " mitten 0.00 0.00 0.00 0\n", " mixing bowl 0.00 0.00 0.00 3\n", " mobile home 0.25 1.00 0.40 1\n", " modem 0.00 0.00 0.00 0\n", " monarch 1.00 1.00 1.00 1\n", " monastery 1.00 1.00 1.00 1\n", " mongoose 0.00 0.00 0.00 1\n", " monitor 0.00 0.00 0.00 1\n", " moped 0.00 0.00 0.00 1\n", " mortar 0.50 1.00 0.67 1\n", " mortarboard 0.00 0.00 0.00 2\n", " mosque 1.00 1.00 1.00 1\n", " mosquito net 0.00 0.00 0.00 1\n", " motor scooter 0.00 0.00 0.00 1\n", " mountain bike 1.00 1.00 1.00 1\n", " mountain tent 1.00 1.00 1.00 1\n", " mouse 0.00 0.00 0.00 1\n", " moving van 0.00 0.00 0.00 1\n", " mud turtle 0.00 0.00 0.00 1\n", " mushroom 0.00 0.00 0.00 0\n", " nail 0.25 1.00 0.40 1\n", " neck brace 0.00 0.00 0.00 1\n", " necklace 1.00 0.50 0.67 2\n", " nematode 0.00 0.00 0.00 1\n", " nipple 0.00 0.00 0.00 1\n", " notebook 1.00 0.50 0.67 2\n", " obelisk 1.00 0.33 0.50 3\n", " oboe 1.00 1.00 1.00 1\n", " ocarina 0.00 0.00 0.00 1\n", " odometer 1.00 1.00 1.00 1\n", " oil filter 0.00 0.00 0.00 1\n", " orange 1.00 1.00 1.00 1\n", " orangutan 1.00 1.00 1.00 1\n", " oscilloscope 0.50 0.50 0.50 2\n", " ostrich 1.00 1.00 1.00 1\n", " otter 0.00 0.00 0.00 1\n", " overskirt 0.00 0.00 0.00 1\n", " ox 1.00 1.00 1.00 1\n", " oxcart 1.00 1.00 1.00 1\n", " oxygen mask 0.00 0.00 0.00 1\n", " oystercatcher 1.00 1.00 1.00 1\n", " packet 0.00 0.00 0.00 1\n", " paddle 0.00 0.00 0.00 0\n", " padlock 0.33 0.50 0.40 2\n", " paintbrush 0.00 0.00 0.00 1\n", " pajama 0.50 0.50 0.50 2\n", " palace 1.00 1.00 1.00 1\n", " panpipe 0.00 0.00 0.00 1\n", " paper towel 1.00 0.50 0.67 2\n", " papillon 1.00 1.00 1.00 1\n", " parachute 1.00 1.00 1.00 1\n", " park bench 0.67 1.00 0.80 2\n", " parking meter 0.00 0.00 0.00 1\n", " partridge 1.00 0.50 0.67 2\n", " passenger car 0.50 1.00 0.67 1\n", " patas 1.00 0.33 0.50 3\n", " patio 0.00 0.00 0.00 1\n", " pay-phone 1.00 1.00 1.00 1\n", " peacock 0.00 0.00 0.00 1\n", " pedestal 0.00 0.00 0.00 0\n", " pelican 1.00 1.00 1.00 1\n", " pencil box 1.00 0.50 0.67 2\n", " pencil sharpener 0.00 0.00 0.00 1\n", " perfume 1.00 1.00 1.00 1\n", " photocopier 1.00 1.00 1.00 1\n", " pick 0.50 1.00 0.67 1\n", " pickelhaube 1.00 1.00 1.00 1\n", " picket fence 1.00 0.50 0.67 2\n", " pickup 0.00 0.00 0.00 1\n", " pier 0.00 0.00 0.00 1\n", " piggy bank 0.00 0.00 0.00 1\n", " pill bottle 0.00 0.00 0.00 1\n", " pillow 0.00 0.00 0.00 0\n", " pineapple 0.00 0.00 0.00 1\n", " ping-pong ball 0.50 1.00 0.67 1\n", " pinwheel 1.00 1.00 1.00 1\n", " pirate 1.00 1.00 1.00 1\n", " pitcher 1.00 0.50 0.67 2\n", " pizza 1.00 1.00 1.00 1\n", " plane 0.00 0.00 0.00 1\n", " planetarium 1.00 1.00 1.00 1\n", " plastic bag 0.00 0.00 0.00 2\n", " plate 0.00 0.00 0.00 1\n", " platypus 0.50 1.00 0.67 1\n", " pole 0.00 0.00 0.00 1\n", " polecat 0.00 0.00 0.00 1\n", " pomegranate 1.00 1.00 1.00 1\n", " poncho 0.50 1.00 0.67 1\n", " pool table 1.00 1.00 1.00 1\n", " pop bottle 0.00 0.00 0.00 1\n", " porcupine 0.33 1.00 0.50 1\n", " pot 1.00 0.50 0.67 2\n", " potpie 0.00 0.00 0.00 1\n", " potter's wheel 1.00 1.00 1.00 1\n", " power drill 0.00 0.00 0.00 1\n", " prairie chicken 1.00 1.00 1.00 1\n", " prayer rug 0.33 1.00 0.50 1\n", " printer 1.00 0.50 0.67 2\n", " prison 1.00 1.00 1.00 1\n", " proboscis monkey 1.00 1.00 1.00 1\n", " projector 0.00 0.00 0.00 0\n", " promontory 1.00 1.00 1.00 2\n", " ptarmigan 1.00 1.00 1.00 1\n", " puffer 0.00 0.00 0.00 1\n", " pug 1.00 0.50 0.67 2\n", " punching bag 0.00 0.00 0.00 1\n", " purse 0.00 0.00 0.00 2\n", " quail 1.00 1.00 1.00 1\n", " quill 0.00 0.00 0.00 2\n", " racer 0.00 0.00 0.00 1\n", " racket 1.00 1.00 1.00 1\n", " radiator 1.00 1.00 1.00 1\n", " radio 1.00 1.00 1.00 1\n", " radio telescope 0.50 1.00 0.67 1\n", " rain barrel 0.00 0.00 0.00 1\n", " ram 0.00 0.00 0.00 1\n", " rapeseed 1.00 1.00 1.00 1\n", " recreational vehicle 1.00 1.00 1.00 1\n", " red fox 0.33 0.50 0.40 2\n", " red wine 0.00 0.00 0.00 0\n", " red wolf 0.00 0.00 0.00 2\n", " red-backed sandpiper 0.00 0.00 0.00 1\n", " red-breasted merganser 1.00 1.00 1.00 1\n", " redbone 0.00 0.00 0.00 1\n", " redshank 0.50 1.00 0.67 1\n", " reel 0.00 0.00 0.00 1\n", " reflex camera 0.00 0.00 0.00 0\n", " refrigerator 0.00 0.00 0.00 0\n", " restaurant 0.00 0.00 0.00 3\n", " revolver 1.00 1.00 1.00 1\n", " rifle 0.00 0.00 0.00 2\n", " ringlet 0.50 1.00 0.67 1\n", " ringneck snake 0.00 0.00 0.00 1\n", " robin 0.50 1.00 0.67 1\n", " rock beauty 0.00 0.00 0.00 0\n", " rock crab 0.00 0.00 0.00 1\n", " rock python 0.00 0.00 0.00 1\n", " rocking chair 1.00 1.00 1.00 1\n", " rotisserie 1.00 0.50 0.67 2\n", " rubber eraser 0.00 0.00 0.00 1\n", " ruddy turnstone 1.00 1.00 1.00 1\n", " ruffed grouse 0.00 0.00 0.00 0\n", " rugby ball 0.00 0.00 0.00 1\n", " rule 1.00 0.50 0.67 2\n", " running shoe 0.00 0.00 0.00 1\n", " safe 0.50 0.50 0.50 2\n", " safety pin 1.00 1.00 1.00 2\n", " saltshaker 0.00 0.00 0.00 0\n", " sandal 1.00 1.00 1.00 1\n", " sandbar 1.00 1.00 1.00 1\n", " sarong 0.00 0.00 0.00 1\n", " sax 0.00 0.00 0.00 1\n", " scabbard 1.00 0.33 0.50 3\n", " scale 0.00 0.00 0.00 1\n", " schipperke 0.50 1.00 0.67 2\n", " school bus 0.50 1.00 0.67 1\n", " scoreboard 0.33 1.00 0.50 1\n", " scorpion 0.00 0.00 0.00 1\n", " screw 1.00 1.00 1.00 1\n", " scuba diver 1.00 1.00 1.00 1\n", " sea anemone 0.00 0.00 0.00 0\n", " sea cucumber 1.00 1.00 1.00 1\n", " sea lion 0.50 1.00 0.67 1\n", " sea slug 1.00 1.00 1.00 1\n", " sea urchin 0.00 0.00 0.00 1\n", " seat belt 0.25 1.00 0.40 1\n", " sewing machine 0.00 0.00 0.00 1\n", " shield 0.00 0.00 0.00 0\n", " shoe shop 1.00 1.00 1.00 1\n", " shoji 1.00 1.00 1.00 1\n", " shopping basket 0.50 1.00 0.67 1\n", " shopping cart 0.00 0.00 0.00 0\n", " shovel 0.00 0.00 0.00 1\n", " shower cap 0.33 1.00 0.50 1\n", " shower curtain 0.50 1.00 0.67 1\n", " siamang 0.00 0.00 0.00 2\n", " sidewinder 0.00 0.00 0.00 1\n", " silky terrier 1.00 1.00 1.00 1\n", " ski 1.00 0.50 0.67 2\n", " ski mask 0.00 0.00 0.00 0\n", " skunk 0.50 1.00 0.67 2\n", " sleeping bag 0.50 1.00 0.67 1\n", " slide rule 0.00 0.00 0.00 1\n", " sliding door 0.67 1.00 0.80 2\n", " slot 0.50 1.00 0.67 1\n", " sloth bear 1.00 1.00 1.00 1\n", " slug 0.00 0.00 0.00 1\n", " snail 0.00 0.00 0.00 1\n", " snorkel 1.00 0.50 0.67 2\n", " snow leopard 1.00 1.00 1.00 1\n", " snowmobile 1.00 1.00 1.00 1\n", " snowplow 0.50 1.00 0.67 1\n", " soap dispenser 0.00 0.00 0.00 3\n", " soccer ball 1.00 1.00 1.00 1\n", " soft-coated wheaten terrier 1.00 0.33 0.50 3\n", " solar dish 0.00 0.00 0.00 1\n", " sombrero 0.00 0.00 0.00 1\n", " sorrel 0.50 0.50 0.50 2\n", " soup bowl 0.50 1.00 0.67 1\n", " spaghetti squash 0.00 0.00 0.00 1\n", " speedboat 1.00 1.00 1.00 1\n", " spider web 1.00 1.00 1.00 1\n", " spindle 0.00 0.00 0.00 0\n", " spiny lobster 1.00 1.00 1.00 1\n", " spoonbill 1.00 1.00 1.00 1\n", " sports car 0.00 0.00 0.00 1\n", " spotlight 0.00 0.00 0.00 1\n", " spotted salamander 1.00 1.00 1.00 1\n", " squirrel monkey 0.00 0.00 0.00 1\n", " stage 0.25 1.00 0.40 1\n", " standard schnauzer 0.00 0.00 0.00 1\n", " starfish 0.00 0.00 0.00 1\n", " steam locomotive 0.50 1.00 0.67 1\n", " steel arch bridge 0.00 0.00 0.00 0\n", " steel drum 0.50 1.00 0.67 1\n", " stethoscope 0.00 0.00 0.00 1\n", " stingray 0.50 1.00 0.67 1\n", " stinkhorn 0.00 0.00 0.00 1\n", " stole 0.00 0.00 0.00 1\n", " stone wall 1.00 1.00 1.00 1\n", " stove 0.50 1.00 0.67 1\n", " strainer 0.50 1.00 0.67 1\n", " strawberry 0.00 0.00 0.00 1\n", " street sign 0.00 0.00 0.00 1\n", " streetcar 0.50 1.00 0.67 1\n", " stretcher 0.00 0.00 0.00 1\n", " studio couch 1.00 1.00 1.00 1\n", " stupa 1.00 1.00 1.00 1\n", " sturgeon 0.00 0.00 0.00 1\n", " submarine 0.00 0.00 0.00 1\n", " suit 0.00 0.00 0.00 1\n", " sulphur butterfly 1.00 1.00 1.00 1\n", " sulphur-crested cockatoo 0.00 0.00 0.00 1\n", " sundial 0.00 0.00 0.00 0\n", " sunglass 0.00 0.00 0.00 1\n", " sunscreen 1.00 0.50 0.67 2\n", " suspension bridge 0.00 0.00 0.00 1\n", " swab 0.00 0.00 0.00 0\n", " sweatshirt 0.00 0.00 0.00 0\n", " swimming trunks 0.50 1.00 0.67 1\n", " swing 0.00 0.00 0.00 3\n", " switch 0.00 0.00 0.00 0\n", " syringe 0.00 0.00 0.00 1\n", " tabby 1.00 0.50 0.67 2\n", " table lamp 0.50 1.00 0.67 1\n", " tailed frog 0.50 0.50 0.50 2\n", " tank 1.00 0.50 0.67 2\n", " tape player 0.00 0.00 0.00 2\n", " tarantula 1.00 1.00 1.00 1\n", " teapot 1.00 1.00 1.00 1\n", " teddy 1.00 1.00 1.00 1\n", " television 0.00 0.00 0.00 1\n", " tench 1.00 1.00 1.00 1\n", " tennis ball 0.50 1.00 0.67 1\n", " terrapin 1.00 0.67 0.80 3\n", " thatch 0.00 0.00 0.00 1\n", " theater curtain 0.50 1.00 0.67 1\n", " thimble 0.00 0.00 0.00 2\n", " three-toed sloth 0.50 1.00 0.67 1\n", " throne 0.50 1.00 0.67 1\n", " thunder snake 0.00 0.00 0.00 1\n", " tick 0.00 0.00 0.00 0\n", " tiger 1.00 1.00 1.00 2\n", " tiger beetle 0.00 0.00 0.00 1\n", " tiger shark 1.00 1.00 1.00 2\n", " tile roof 1.00 1.00 1.00 1\n", " timber wolf 0.00 0.00 0.00 1\n", " toaster 0.00 0.00 0.00 0\n", " toilet seat 1.00 1.00 1.00 2\n", " toilet tissue 0.20 0.50 0.29 2\n", " totem pole 0.50 1.00 0.67 1\n", " toucan 1.00 1.00 1.00 1\n", " tow truck 0.50 1.00 0.67 1\n", " toy poodle 0.00 0.00 0.00 1\n", " toyshop 0.00 0.00 0.00 1\n", " tractor 1.00 1.00 1.00 1\n", " traffic light 1.00 0.50 0.67 2\n", " trailer truck 1.00 1.00 1.00 1\n", " tray 0.00 0.00 0.00 1\n", " tree frog 1.00 1.00 1.00 1\n", " trench coat 0.00 0.00 0.00 1\n", " triceratops 0.00 0.00 0.00 2\n", " tricycle 0.00 0.00 0.00 3\n", " trifle 1.00 1.00 1.00 1\n", " trilobite 0.50 1.00 0.67 1\n", " trimaran 1.00 1.00 1.00 1\n", " tripod 1.00 1.00 1.00 1\n", " triumphal arch 0.50 1.00 0.67 1\n", " trolleybus 0.00 0.00 0.00 1\n", " trombone 0.00 0.00 0.00 1\n", " turnstile 0.00 0.00 0.00 1\n", " tusker 1.00 0.50 0.67 2\n", " typewriter keyboard 1.00 0.50 0.67 2\n", " umbrella 0.50 0.50 0.50 2\n", " unicycle 0.50 1.00 0.67 1\n", " upright 0.50 1.00 0.67 1\n", " vacuum 1.00 0.50 0.67 2\n", " valley 1.00 0.50 0.67 2\n", " vase 0.00 0.00 0.00 0\n", " vault 1.00 0.33 0.50 3\n", " velvet 0.00 0.00 0.00 1\n", " vending machine 1.00 1.00 1.00 1\n", " viaduct 1.00 1.00 1.00 1\n", " vine snake 0.00 0.00 0.00 1\n", " violin 1.00 1.00 1.00 1\n", " vizsla 0.00 0.00 0.00 1\n", " volcano 1.00 1.00 1.00 1\n", " volleyball 1.00 1.00 1.00 1\n", " vulture 0.00 0.00 0.00 1\n", " waffle iron 0.00 0.00 0.00 1\n", " walking stick 0.00 0.00 0.00 1\n", " wall clock 1.00 1.00 1.00 2\n", " wallaby 1.00 1.00 1.00 1\n", " wallet 0.00 0.00 0.00 0\n", " wardrobe 0.50 1.00 0.67 1\n", " warplane 0.00 0.00 0.00 0\n", " warthog 0.50 1.00 0.67 1\n", " washbasin 0.00 0.00 0.00 3\n", " washer 1.00 1.00 1.00 1\n", " water buffalo 1.00 0.50 0.67 2\n", " water jug 0.00 0.00 0.00 2\n", " water ouzel 1.00 1.00 1.00 1\n", " water snake 1.00 1.00 1.00 1\n", " water tower 1.00 1.00 1.00 2\n", " weasel 0.00 0.00 0.00 0\n", " web site 1.00 0.33 0.50 3\n", " weevil 0.00 0.00 0.00 1\n", " whiptail 0.00 0.00 0.00 2\n", " whiskey jug 1.00 1.00 1.00 1\n", " whistle 1.00 1.00 1.00 1\n", " white stork 1.00 1.00 1.00 1\n", " wig 0.00 0.00 0.00 1\n", " wild boar 0.00 0.00 0.00 1\n", " window screen 1.00 0.50 0.67 2\n", " window shade 1.00 1.00 1.00 1\n", " wine bottle 0.00 0.00 0.00 1\n", " wing 1.00 1.00 1.00 1\n", " wire-haired fox terrier 1.00 1.00 1.00 1\n", " wolf spider 1.00 0.50 0.67 2\n", " wombat 0.00 0.00 0.00 1\n", " wood rabbit 0.00 0.00 0.00 0\n", " wooden spoon 0.50 0.50 0.50 2\n", " wool 0.00 0.00 0.00 2\n", " worm fence 1.00 1.00 1.00 1\n", " wreck 0.50 1.00 0.67 1\n", " yawl 1.00 1.00 1.00 1\n", " yellow lady's slipper 1.00 1.00 1.00 1\n", " yurt 1.00 1.00 1.00 1\n", " zebra 0.00 0.00 0.00 1\n", " zucchini 1.00 1.00 1.00 1\n", "\n", " accuracy 0.54 1000\n", " macro avg 0.47 0.52 0.47 1000\n", " weighted avg 0.53 0.54 0.51 1000\n", "\n" ] } ], "source": [ "eval_key = \"class_eval\"\n", " # Now evaluate on the \"defect2\" field\n", "eval_classif_padim = dataset.evaluate_classifications(\n", " \"predictions\",\n", " gt_field=\"ground_truth\",\n", " method=\"simple\", #method is important to see data in the FO app\n", " eval_key=eval_key, # <--- store this run under \"classification_eval\"\n", ")\n", "eval_classif_padim.print_report()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![imagenet_evaluation](https://cdn.voxel51.com/getting_started_model_dataset_zoo/notebook2/imagenet_evaluation.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "FiftyOne Zoo simplifies the process of working with computer vision datasets and models. It provides a valuable resource for researchers, developers, and enthusiasts.\n", "\n", "### Further Exploration:\n", "* Explore the [FiftyOne documentation](https://docs.voxel51.com/) for more advanced features.\n", "* Try different datasets and models from the Zoo.\n", "* Integrate FiftyOne Zoo into your computer vision workflows.\n", "\n", "## Next Steps\n", "\n", "To continue exploring, check out:\n", "- [Getting Started with FiftyOne](https://beta-docs.voxel51.com/getting_started/)\n", "- [Other Datasets](https://beta-docs.voxel51.com/data/dataset_zoo/)\n", "- [Other Models](https://beta-docs.voxel51.com/models/model_zoo/)\n", "- Join our [Discord community](https://community.voxel51.com)\n", "- Follow us on [LinkedIn](https://www.linkedin.com/company/voxel51/)" ] } ], "metadata": { "kernelspec": { "display_name": "env", "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": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 2 }