Datasets:
metadata
dataset_info:
features:
- name: image
dtype: image
- name: bounding_boxes
sequence:
sequence: float64
- name: classes
sequence: int64
splits:
- name: train
num_bytes: 147837671.888
num_examples: 2596
- name: validation
num_bytes: 26070707
num_examples: 458
download_size: 172845541
dataset_size: 173908378.888
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
license: apache-2.0
task_categories:
- image-classification
pretty_name: FLIR IR YOLO expansion
size_categories:
- 1K<n<10K
FLIR IR YOLO expansion
Images from repository FLIR_IR_Expansion
This expansion pack is prepared specifically for training a YOU-ONLY-LOOK-ONCE(YOLO) network. All frames are labeled in the YOLO format. If you want to use this expansion pack for other purposes, images are still available for download but requires manual labeling
Download all dataset
If you want to download all dataset you must do
from datasets import load_dataset
dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion")
If you only want to download train
or validation
split you must do
from datasets import load_dataset
train_dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion", split='train')
validation_dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion", split='validation')
Download by stream
If you want to donwload dataset by stream, you must do
from datasets import load_dataset
iterable_dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion", streaming=True)
now you can get every sample by
for sample in iterable_dataset['train']:
print(sample['bounding_boxes'], sample['classes'])
example['sample'].show()
break
or with
sample = next(iter(iterable_dataset['train']))
print(sample['bounding_boxes'], sample['classes'])
sample['image']
If you want to get a batch of samples you ca do
BS = 4
example_batch = list(iterable_dataset['train'].take(BS))