Instructions to use OpenGVLab/internimage_b_1k_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/internimage_b_1k_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OpenGVLab/internimage_b_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_b_1k_224", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4395b3ab0dfeed72b9ca8e89af784d30ba42b370b4d10cdbf2ffdb8f8ca8c35c
- Size of remote file:
- 390 MB
- SHA256:
- a8b27b560753220a9a16e8c95b8d80c36e72230aa7652da10c1097e8ac771181
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