Instructions to use GameBotCreator/sam3-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GameBotCreator/sam3-fp16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="GameBotCreator/sam3-fp16")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("GameBotCreator/sam3-fp16") model = AutoModel.from_pretrained("GameBotCreator/sam3-fp16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
SAM 3 (fp16)
A float16 copy of Meta's SAM 3 (Segment Anything Model 3) in Hugging Face Transformers format, used by Game Bot Creator to auto-label images on the user's own PC.
This is not an official Meta release and is not made, endorsed or supported by Meta. It is our conversion of the SAM 3 weights from facebook/sam3 (revision 3c879f39826c281e95690f02c7821c4de09afae7) to float16. Nothing else was changed: same architecture, same tokenizer and processor settings. Download size is about 1.7 GB instead of 3.4 GB; in our tests detection results matched the original.
License
SAM 3 is licensed under the SAM License (see LICENSE, copied unchanged from facebook/sam3). This copy, as a derivative of the SAM Materials, is distributed under the same license. By downloading or using it you agree to the SAM License, including its Acceptable Use, trade-control and prohibited-use terms.
Any use of these weights must follow the SAM License. If you redistribute them, include the LICENSE file.
Usage
import torch
from transformers import Sam3Processor, Sam3Model
model = Sam3Model.from_pretrained("GameBotCreator/sam3-fp16", torch_dtype=torch.float16).to("cuda")
processor = Sam3Processor.from_pretrained("GameBotCreator/sam3-fp16")
On CPU, load with torch_dtype=torch.float32 (the weights are upcast on load).
Credits
SAM 3 was created by Meta AI. See the original model card at facebook/sam3 and the paper and code at facebookresearch/sam3.
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