Instructions to use AndreCosta/resnet-unet-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreCosta/resnet-unet-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="AndreCosta/resnet-unet-segmentation")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AndreCosta/resnet-unet-segmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scripts/Dataset/getDS_HuggingFace.py from AndreCosta/resnet-unet-segmentation: direct link, hf CLI and curl.
- Browser
- Download file 627 Bytes
-
https://huggingface.co/AndreCosta/resnet-unet-segmentation/resolve/main/scripts/Dataset/getDS_HuggingFace.py
- Command line
-
hf download hf://AndreCosta/resnet-unet-segmentation/scripts/Dataset/getDS_HuggingFace.py
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curl -L -o getDS_HuggingFace.py https://huggingface.co/AndreCosta/resnet-unet-segmentation/resolve/main/scripts/Dataset/getDS_HuggingFace.py
627 Bytes
| import os | |
| from datasets import load_dataset | |
| dataset_name = 'Onegafer/vehicle_segmentation' | |
| split = "train" # ou "test", "validation", etc. | |
| print(f"🔽 Baixando o dataset: {dataset_name}...") | |
| dataset = load_dataset(dataset_name, split=split) | |
| print(f"✅ Dataset carregado com {len(dataset)} amostras.") | |
| print("Exemplo:", dataset[0]) | |
| output_dir = '/home/pi/Deposito/Projetos/Meus/CertificacaoHuggingFace/fontes/TempDataSet/' | |
| os.makedirs(output_dir, exist_ok=True) | |
| dataset.save_to_disk(os.path.join(output_dir, dataset_name.replace("/", "_"))) | |
| print(f"💾 Dataset salvo em: {output_dir}/{dataset_name.replace('/', '_')}") | |