---
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](https://github.com/sensationTI/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))
```