Image Segmentation
Transformers
Safetensors
sam2
instance-segmentation
panoptic-segmentation
semantic-segmentation
zero-shot
open-vocabulary
beit3
fiftyone
Instructions to use Voxel51/openworld-sam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Voxel51/openworld-sam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Voxel51/openworld-sam")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Voxel51/openworld-sam", device_map="auto") - sam2
How to use Voxel51/openworld-sam with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(Voxel51/openworld-sam) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained(Voxel51/openworld-sam) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
File size: 883 Bytes
98405c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
def get_extensions():
srcs = ["sam2/csrc/connected_components.cu"]
compile_args = {
"cxx": [],
"nvcc": [
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
],
}
ext_modules = [CUDAExtension("sam2._C", srcs, extra_compile_args=compile_args)]
return ext_modules
# Setup configuration
setup(
ext_modules=get_extensions(),
cmdclass={"build_ext": BuildExtension.with_options(no_python_abi_suffix=True)},
)
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