Instructions to use frgfm/cspdarknet53_mish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/cspdarknet53_mish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/cspdarknet53_mish") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("frgfm/cspdarknet53_mish", dtype="auto") - Notebooks
- Google Colab
- Kaggle
fg-mindee commited on
Commit ·
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Parent(s): 7910142
docs: Updated README
Browse files
README.md
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@@ -56,7 +56,7 @@ from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToT
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from torchvision.transforms.functional import InterpolationMode
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from holocron.models import model_from_hf_hub
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model = model_from_hf_hub("frgfm/
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img = Image.open(path_to_an_image).convert("RGB")
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from torchvision.transforms.functional import InterpolationMode
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from holocron.models import model_from_hf_hub
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model = model_from_hf_hub("frgfm/cspdarknet53_mish").eval()
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img = Image.open(path_to_an_image).convert("RGB")
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