datasets library
Browse files
README.md
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@@ -269,28 +269,30 @@ The dataset is organized in the following structure:
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**Note**: Since the same viewpoint is reproduced across each weather variation, hence ClearNoon annotations can be used for all images pertaining to the different weather variations.
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## Dataset Download
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The dataset can be downloaded using both [datasets](https://huggingface.co/docs/datasets/index) library by Hugging Face and wget. Since SkyScenes offers variations across different axes
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hence we enable different subsets for download that can aid in model sensitivity analysis across these axes.
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**Example script for downloading different subsets of data using wget**
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```
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#!/bin/bash
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#Change here to download a specific Height and Pitch Variation, for example - H_15_P_0
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HP=('H_15_P_0' 'H_15_P_45' 'H_15_P_60' 'H_15_P_90' 'H_35_P_0' 'H_35_P_45' 'H_35_P_60' 'H_35_P_90' 'H_60_P_0' 'H_60_P_45' 'H_60_P_60' 'H_60_P_90')
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#Change here to download a specific weather subset, for example - ClearNoon
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weather=('ClearNoon' 'ClearNight' 'ClearSunset' 'CloudyNoon' 'MidRainyNoon')
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#Change here to download a specific Town subset, for example - Town07
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layout=('Town01' 'Town02' 'Town03' 'Town04' 'Town05' 'Town06' 'Town07' 'Town10HD')
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#Change here for any specific annotation, for example - https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Segment
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base_url=('https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images'
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#Change here for base download folder
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base_download_folder='SkyScenes'
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for hp in "${HP[@]}"; do
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for w in "${weather[@]}"; do
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for t in "${layout[@]}"; do
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# wget -P "$download_folder" "$download_url"
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done
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done
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done
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done
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```
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###
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**Note**: Since the same viewpoint is reproduced across each weather variation, hence ClearNoon annotations can be used for all images pertaining to the different weather variations.
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## Dataset Download
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The dataset can be downloaded using both [datasets](https://huggingface.co/docs/datasets/index) library by Hugging Face and wget. Since SkyScenes offers variations across different axes
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hence we enable different subsets for download that can aid in model sensitivity analysis across these axes.
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### Download instructions: wget
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**Example script for downloading different subsets of data using wget**
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```bash
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#!/bin/bash
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#Change here to download a specific Height and Pitch Variation, for example - H_15_P_0
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#Note- Currently supporting only H_35_P_45 for Instance and Depth Maps
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HP=('H_15_P_0' 'H_15_P_45' 'H_15_P_60' 'H_15_P_90' 'H_35_P_0' 'H_35_P_45' 'H_35_P_60' 'H_35_P_90' 'H_60_P_0' 'H_60_P_45' 'H_60_P_60' 'H_60_P_90')
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#Change here to download a specific weather subset, for example - ClearNoon
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#Note - For Segment, Instance and Depth annotations this field should only have ClearNoon variation
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weather=('ClearNoon' 'ClearNight' 'ClearSunset' 'CloudyNoon' 'MidRainyNoon')
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#Change here to download a specific Town subset, for example - Town07
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layout=('Town01' 'Town02' 'Town03' 'Town04' 'Town05' 'Town06' 'Town07' 'Town10HD')
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#Change here for any specific annotation, for example - https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Segment
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base_url=('https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images')
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#Change here for base download folder
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base_download_folder='SkyScenes'
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for hp in "${HP[@]}"; do
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for w in "${weather[@]}"; do
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for t in "${layout[@]}"; do
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folder=$(echo "$base_url" | awk -F '/' '{print $(NF)}')
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download_url="${base_url}/${hp}/${w}/${t}/${t}.tar.gz"
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download_folder="${base_download_folder}/${folder}/${HP}/${w}/${t}"
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mkdir -p "$download_folder"
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echo "Downloading: $download_url"
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echo "SAving: $download_folder"
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wget -P "$download_folder" "$download_url"
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done
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done
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done
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done
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```
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### Download instructions: [datasets](https://huggingface.co/docs/datasets/index)
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<details>
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<summary>Click to view all the available keys for downloading subsets of the data</summary>
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* **Layout Variations**
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- Rural
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- Urban
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* **Weather Variations**
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- ClearNoon
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- ClearNight (only images)
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- ClearSunset (only images)
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- CloudyNoon (only images)
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- MidRainyNoon (only images)
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* **Height Variations**
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- H_15
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- H_35
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- H_60
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* **Pitch Variations**
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- P_0
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- P_45
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- P_60
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- P_90
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* **Height and Pitch Variations**
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- H_15_P_0
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- H_15_P_45
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- H_15_P_60
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- H_15_P_90
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- H_35_P_0
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- H_35_P_45
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- H_35_P_60
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- H_35_P_90
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- H_60_P_0
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- H_60_P_45
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- H_60_P_60
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- H_60_P_90
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For downloading full dataset key is full
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**💡Notes**:
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- To download **images** append subset key with **images**, example - ```H_35_P_45 images```
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- To download **semantic segmentation** maps append subset key with **semseg**, example - ```H_35_P_45 semseg```
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- To download **instance segmentation** maps append subset key with **instance**, example - ```H_35_P_45 instance```
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- To download **depth** maps append subset key with **depth**, example - ```H_35_P_45 depth```
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</details>
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**Example script for loading H_35_P_45 images**
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```python
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from datasets import load_dataset
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dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 images")
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```
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**Example script for loading H_35_P_45 semantic segmentation maps**
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```python
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from datasets import load_dataset
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dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 semseg")
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```
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**Example script for loading H_35_P_45 instance segmentation maps**
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```python
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from datasets import load_dataset
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dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 instance")
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```
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**Example script for loading H_35_P_45 depth maps**
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```python
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from datasets import load_dataset
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dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 depth")
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```
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### 💡 Notes
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- Depth and Instance segmentation maps are available for only H_35_P_45, other variations will be made available soon.
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- To prevent issues when loading datasets using [datasets](https://huggingface.co/docs/datasets/index) library, it is recommended to avoid downloading subsets that contain overlapping directories. If there are any overlapping directories between the existing downloads and new ones, it's essential to clear the .cache directory of any such overlaps before proceeding with the new downloads. This step will ensure a clean and conflict-free environment for handling datasets.
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