Instructions to use SvNext/GambleENTItTyDETR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SvNext/GambleENTItTyDETR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="SvNext/GambleENTItTyDETR")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("SvNext/GambleENTItTyDETR") model = AutoModelForObjectDetection.from_pretrained("SvNext/GambleENTItTyDETR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SvNext/GambleENTItTyDETR: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/SvNext/GambleENTItTyDETR/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SvNext/GambleENTItTyDETR/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SvNext/GambleENTItTyDETR/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 4ca759a4af988c2aa64fac39f31e54522fd5f7552fbcd49bf8f64153634b5a5c
- Size of remote file:
- 167 MB
- SHA256:
- 38b2d1cd511c6c352c0ffbaca5cf2fb195a58c989c4c03ca90aedc8446918df8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.