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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca66a28d5547b1573efbba8b98eab86d50f2a0c007481a6e45742ccb3367d6c3
- Size of remote file:
- 120 MB
- SHA256:
- d190e409a63dbe3c2b8a1ccbc77298ba067c85a1fb7cdb330a1706596a947429
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.