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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: bounding_boxes |
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sequence: |
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sequence: float64 |
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- name: classes |
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sequence: int64 |
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splits: |
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- name: train |
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num_bytes: 147837671.888 |
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num_examples: 2596 |
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- name: validation |
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num_bytes: 26070707 |
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num_examples: 458 |
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download_size: 172845541 |
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dataset_size: 173908378.888 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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license: apache-2.0 |
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task_categories: |
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- image-classification |
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pretty_name: FLIR IR YOLO expansion |
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size_categories: |
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- 1K<n<10K |
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--- |
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# FLIR IR YOLO expansion |
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Images from repository [FLIR_IR_Expansion](https://github.com/sensationTI/FLIR_IR_Expansion) |
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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 |
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## Download all dataset |
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If you want to download all dataset you must do |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion") |
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``` |
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If you only want to download `train` or `validation` split you must do |
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``` |
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from datasets import load_dataset |
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train_dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion", split='train') |
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validation_dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion", split='validation') |
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``` |
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## Download by stream |
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If you want to donwload dataset by stream, you must do |
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``` |
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from datasets import load_dataset |
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iterable_dataset = load_dataset("SAE-AAI/FLIR_IR_Expansion", streaming=True) |
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``` |
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now you can get every sample by |
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``` |
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for sample in iterable_dataset['train']: |
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print(sample['bounding_boxes'], sample['classes']) |
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example['sample'].show() |
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break |
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``` |
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or with |
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``` |
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sample = next(iter(iterable_dataset['train'])) |
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print(sample['bounding_boxes'], sample['classes']) |
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sample['image'] |
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``` |
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If you want to get a batch of samples you ca do |
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``` |
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BS = 4 |
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example_batch = list(iterable_dataset['train'].take(BS)) |
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``` |
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