--- license: mit datasets: - name: cloud_sky_vis task: type: image-classification files: - name: image_metadata.csv format: csv split: train features: - name: index dtype: int32 - name: filename dtype: string - name: label dtype: string - name: transform_shape dtype: string - name: transform_min dtype: float32 - name: transform_max dtype: float32 --- # Cloud and Sky Image Tensors for Classification This dataset is designed for those interested in cloud classification projects. Due to licensing restrictions, the raw images cannot be shared publicly. However, the transformed tensors provided here are optimized for image classification tasks and are typically all you need for such projects. ### Tensor Specifications These tensors are preprocessed and normalized for use with ResNet models. The normalization parameters are as follows: - **Mean**: `[0.485, 0.456, 0.406]` - **Standard Deviation**: `[0.229, 0.224, 0.225]` ### Cloud Class Labels The dataset uses standard World Meteorological Organization (WMO) cloud classification labels, with the addition of "Clr" for clear skies. The labels used are: - `As`: Altostratus - `Cb`: Cumulonimbus - `Cc`: Cirrocumulus - `Ci`: Cirrus - `Cs`: Cirrostratus - `Ct`: Contrails - `Cu`: Cumulus - `Ns`: Nimbostratus - `Sc`: Stratocumulus - `St`: Stratus - `Ac`: Altocumulus - `Clr`: Clear Sky For more information on cloud classification, please refer to the WMO Cloud Atlas: [WMO Cloud Classification](https://cloudatlas.wmo.int/en/cloud-classification-summary.html). ### Dataset Structure - train_images_tensor.pt: Tensor of transformed images. - train_labels_tensor.pt: Corresponding labels for the images. - image_metadata.csv: Metadata detailing each image's properties. ### Usage These tensors are ready for direct use in training or testing ResNet and similar models for cloud classification tasks. I hope this dataset supports your research and projects. To load the dataset: ``` import torch import requests from io import BytesIO base_url = "https://huggingface.co/datasets/jcamier/cloud_sky_vis/resolve/main/" tensor_url = f"{base_url}train_images_tensor.pt" label_url = f"{base_url}train_labels_tensor.pt" # Load the tensors tensors = torch.load(BytesIO(requests.get(tensor_url).content)) labels = torch.load(BytesIO(requests.get(label_url).content)) # Verify the data is loaded correctly print(tensors.shape) print(labels.shape) ``` Enjoy!