Instructions to use Farideh/dataset_model4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Farideh/dataset_model4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Farideh/dataset_model4") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Farideh/dataset_model4") model = AutoModelForImageClassification.from_pretrained("Farideh/dataset_model4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Farideh/dataset_model4: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Farideh/dataset_model4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Farideh/dataset_model4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Farideh/dataset_model4/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- c33cf8768f5442d6e4f712be5cd1b7d4c6d5dfc56dc606dfafe6a402f77f6044
- Size of remote file:
- 343 MB
- SHA256:
- 87a456b16659b29510005fd94ae714c512d1452279c141664fe48563cdf8f4bb
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