Instructions to use MedicalVision/test_remove_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MedicalVision/test_remove_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="MedicalVision/test_remove_2")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("MedicalVision/test_remove_2") model = AutoModelForObjectDetection.from_pretrained("MedicalVision/test_remove_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b6f4ffd491e7df3d0b6424db30542bc8f577e7f38a569405138b6e40b167838
- Size of remote file:
- 25.9 MB
- SHA256:
- 8f335252ac212cc9d0c14214c3ccbacd48f7e9190a8830f414fb0407cda0f5b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.