Instructions to use facebook/sam3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/sam3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="facebook/sam3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("facebook/sam3") model = AutoModel.from_pretrained("facebook/sam3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Request for manual review of rejected access β undergraduate research
Hello SAM team,
My access request to facebook/sam3 was rejected. Could you please review it or reset the request so I can correct any missing information?
I am Hugo Iglesias Pombo, an engineering undergraduate student at Universidad Intercontinental de la Empresa (UIE), Spain.
For my final degree project, I am studying swimming pool detection and segmentation in Spanish PNOA aerial imagery, followed by comparison with cadastral records. I would like to evaluate pretrained SAM 3 against a YOLO segmentation baseline, measuring detection accuracy, false positives, mask quality and inference time.
This is an academic research project. The outputs are candidates for human review, not automated administrative decisions.
Please let me know whether any additional information or corrections to my original application are required. I will comply with the applicable model license and usage terms.
Thank you.