Instructions to use G-dawg/table_exp_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use G-dawg/table_exp_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="G-dawg/table_exp_2")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("G-dawg/table_exp_2") model = AutoModelForObjectDetection.from_pretrained("G-dawg/table_exp_2") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5776db9f95a303298442a686f2e5ffec61456589505d7b844b98a7c071170995
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size 115431184
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