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cls
string | custom_metrics
null | ytrue
list | ypred
list | confs
list | weights
null | ytrue_ids
list | ypred_ids
list | classes
list | missing
string | ious
list | precision
list | recall
list | iou_threshs
list | recall_sweep
list | thresholds
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fiftyone.utils.eval.coco.COCODetectionResults
| null | ["Bike","Bike","Bike","Bike","(none)","Bike","(none)","(none)","Bike","(none)","(none)","Bike","(non(...TRUNCATED) | ["Bike","Bike","Bike","Bike","Bike","Bike","Bike","Bike","Bike","Bike","Bike","Bike","Bike","Bike","(...TRUNCATED) | [0.7057297825813293,0.6409302353858948,0.6333996653556824,0.6216171383857727,0.5913552045822144,0.56(...TRUNCATED) | null | ["6761ea4a4d61e31cfcf638d7","6761ea4a4d61e31cfcf638d6","6761ea4a4d61e31cfcf638da","6761ea4a4d61e31cf(...TRUNCATED) | ["6893b0d3f6fc63749c17a35f","6893b0d3f6fc63749c17a360","6893b0d3f6fc63749c17a361","6893b0d3f6fc63749(...TRUNCATED) |
[
"Bike",
"Bus",
"Car",
"Pedestrian",
"Truck"
] |
(none)
| [0.7075349626452048,0.8116272718385917,0.6701815071336704,0.825129389414052,null,0.5825227869378371,(...TRUNCATED) | [[[1.0,1.0,0.9981785063752276,0.9981785063752276,0.9981785063752276,0.995768688293371,0.994363021420(...TRUNCATED) | [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0(...TRUNCATED) |
[
0.5,
0.55,
0.6,
0.65,
0.7,
0.75,
0.8,
0.85,
0.9,
0.95
] | [[0.49773303619457465,0.6054852320675106,0.5922857142857143,0.17788089713843774,0.1127946127946128],(...TRUNCATED) | [[[0.9069002866744995,0.8749024271965027,0.858780562877655,0.8474629521369934,0.83734530210495,0.827(...TRUNCATED) |
Dataset Card for fisheye8k_ensemble_mapped
This is a FiftyOne dataset with 8000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Abeyankar/test8k_ensemble2")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
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- Language(s) (NLP): en
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