Instructions to use OpenGVLab/internimage_t_1k_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/internimage_t_1k_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/internimage_t_1k_224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/internimage_t_1k_224", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload models
Browse files- modeling_internimage.py +2 -2
modeling_internimage.py
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@@ -853,8 +853,8 @@ class InternImageModel(PreTrainedModel):
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remove_center=config.remove_center, # for InternImage-H/G
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)
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def forward(self,
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return self.model.forward_features(
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class InternImageModelForImageClassification(PreTrainedModel):
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remove_center=config.remove_center, # for InternImage-H/G
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)
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def forward(self, pixel_values):
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return self.model.forward_features(pixel_values)
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class InternImageModelForImageClassification(PreTrainedModel):
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