mohan-aditya05

import fiftyone as fo

ds = fo.Dataset("test-dataset")

print(ds)

mohan-aditya05

import fiftyone as fo

ds = fo.Dataset("test-dataset")

print(ds)

mohan-aditya05

import fiftyone as fo

# create an empty Dataset
ds = fo.Dataset("test-dataset")

print(ds)

mohan-aditya05

from typing import NamedTuple
from typing import List
from typing import Callable, Optional

import fiftyone as fo
import fiftyone.zoo as foz

from pathlib import Path
import json
import numpy as np

mohan-aditya05

import fiftyone as fo

ds = fo.load_dataset("imagenet-preds")

results = ds.evaluate_classifications(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval_simple"
)

mohan-aditya05

import fiftyone as fo
from fiftyone import ViewField as F

dataset = fo.Dataset("imagenet")

# Using array slicing
range_view = dataset[2:5]

dataset.save_view("range_view", range_view)

mohan-aditya05

{
   "0":"tench, Tinca tinca",
   "1":"goldfish, Carassius auratus",
   "2":"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
   "3":"tiger shark, Galeocerdo cuvieri",
   "4":"hammerhead, hammerhead shark",
   "5":"electric ray, crampfish, numbfish, torpedo",
   "6":"stingray",
   "7":"cock",
   "8":"hen",