# SAM 2: Segment Anything in Images and Videos
**[AI at Meta, FAIR](https://ai.meta.com/research/)**
[Nikhila Ravi](https://nikhilaravi.com/), [Valentin Gabeur](https://gabeur.github.io/), [Yuan-Ting Hu](https://scholar.google.com/citations?user=E8DVVYQAAAAJ&hl=en), [Ronghang Hu](https://ronghanghu.com/), [Chaitanya Ryali](https://scholar.google.com/citations?user=4LWx24UAAAAJ&hl=en), [Tengyu Ma](https://scholar.google.com/citations?user=VeTSl0wAAAAJ&hl=en), [Haitham Khedr](https://hkhedr.com/), [Roman Rädle](https://scholar.google.de/citations?user=Tpt57v0AAAAJ&hl=en), [Chloe Rolland](https://scholar.google.com/citations?hl=fr&user=n-SnMhoAAAAJ), [Laura Gustafson](https://scholar.google.com/citations?user=c8IpF9gAAAAJ&hl=en), [Eric Mintun](https://ericmintun.github.io/), [Junting Pan](https://junting.github.io/), [Kalyan Vasudev Alwala](https://scholar.google.co.in/citations?user=m34oaWEAAAAJ&hl=en), [Nicolas Carion](https://www.nicolascarion.com/), [Chao-Yuan Wu](https://chaoyuan.org/), [Ross Girshick](https://www.rossgirshick.info/), [Piotr Dollár](https://pdollar.github.io/), [Christoph Feichtenhofer](https://feichtenhofer.github.io/)
[[`Paper`](https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/)] [[`Project`](https://ai.meta.com/sam2)] [[`Demo`](https://sam2.metademolab.com/)] [[`Dataset`](https://ai.meta.com/datasets/segment-anything-video)] [[`Blog`](https://ai.meta.com/blog/segment-anything-2)] [[`BibTeX`](#citing-sam-2)]
**Segment Anything Model 2 (SAM 2)** is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect [**our SA-V dataset**](https://ai.meta.com/datasets/segment-anything-video), the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains.
## Installation
SAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:
```bash
git clone https://github.com/facebookresearch/segment-anything-2.git
cd segment-anything-2 & pip install -e .
```
If you are installing on Windows, it's strongly recommended to use [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) with Ubuntu.
To use the SAM 2 predictor and run the example notebooks, `jupyter` and `matplotlib` are required and can be installed by:
```bash
pip install -e ".[demo]"
```
Note:
1. It's recommended to create a new Python environment via [Anaconda](https://www.anaconda.com/) for this installation and install PyTorch 2.3.1 (or higher) via `pip` following https://pytorch.org/. If you have a PyTorch version lower than 2.3.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using `pip`.
2. The step above requires compiling a custom CUDA kernel with the `nvcc` compiler. If it isn't already available on your machine, please install the [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) with a version that matches your PyTorch CUDA version.
3. If you see a message like `Failed to build the SAM 2 CUDA extension` during installation, you can ignore it and still use SAM 2 (some post-processing functionality may be limited, but it doesn't affect the results in most cases).
Please see [`INSTALL.md`](./INSTALL.md) for FAQs on potential issues and solutions.
## Getting Started
### Download Checkpoints
First, we need to download a model checkpoint. All the model checkpoints can be downloaded by running:
```bash
cd checkpoints && \
./download_ckpts.sh && \
cd ..
```
or individually from:
- [sam2_hiera_tiny.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt)
- [sam2_hiera_small.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt)
- [sam2_hiera_base_plus.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_base_plus.pt)
- [sam2_hiera_large.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt)
Then SAM 2 can be used in a few lines as follows for image and video prediction.
### Image prediction
SAM 2 has all the capabilities of [SAM](https://github.com/facebookresearch/segment-anything) on static images, and we provide image prediction APIs that closely resemble SAM for image use cases. The `SAM2ImagePredictor` class has an easy interface for image prompting.
```python
import torch
from sam2.build_sam import build_sam2
from sam2.sam2_image_predictor import SAM2ImagePredictor
checkpoint = "./checkpoints/sam2_hiera_large.pt"
model_cfg = "sam2_hiera_l.yaml"
predictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)
```
Please refer to the examples in [image_predictor_example.ipynb](./notebooks/image_predictor_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/segment-anything-2/blob/main/notebooks/image_predictor_example.ipynb)) for static image use cases.
SAM 2 also supports automatic mask generation on images just like SAM. Please see [automatic_mask_generator_example.ipynb](./notebooks/automatic_mask_generator_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/segment-anything-2/blob/main/notebooks/automatic_mask_generator_example.ipynb)) for automatic mask generation in images.
### Video prediction
For promptable segmentation and tracking in videos, we provide a video predictor with APIs for example to add prompts and propagate masklets throughout a video. SAM 2 supports video inference on multiple objects and uses an inference state to keep track of the interactions in each video.
```python
import torch
from sam2.build_sam import build_sam2_video_predictor
checkpoint = "./checkpoints/sam2_hiera_large.pt"
model_cfg = "sam2_hiera_l.yaml"
predictor = build_sam2_video_predictor(model_cfg, checkpoint)
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_or_box(state, <your_prompts>):
# propagate the prompts to get masklets throughout the video
for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
...
```
Please refer to the examples in [video_predictor_example.ipynb](./notebooks/video_predictor_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/segment-anything-2/blob/main/notebooks/video_predictor_example.ipynb)) for details on how to add click or box prompts, make refinements, and track multiple objects in videos.
## Load from 🤗 Hugging Face
Alternatively, models can also be loaded from [Hugging Face](https://huggingface.co/models?search=facebook/sam2) (requires `pip install huggingface_hub`).
For image prediction:
```python
import torch
from sam2.sam2_image_predictor import SAM2ImagePredictor
predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)
```
For video prediction:
```python
import torch
from sam2.sam2_video_predictor import SAM2VideoPredictor
predictor = SAM2VideoPredictor.from_pretrained("facebook/sam2-hiera-large")
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_or_box(state, <your_prompts>):
# propagate the prompts to get masklets throughout the video
for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
...
```
## Model Description
| **Model** | **Size (M)** | **Speed (FPS)** | **SA-V test (J&F)** | **MOSE val (J&F)** | **LVOS v2 (J&F)** |
| :------------------: | :----------: | :--------------------: | :-----------------: | :----------------: | :---------------: |
| sam2_hiera_tiny | 38.9 | 47.2 | 75.0 | 70.9 | 75.3 |
| sam2_hiera_small | 46 | 43.3 (53.0 compiled\*) | 74.9 | 71.5 | 76.4 |
| sam2_hiera_base_plus | 80.8 | 34.8 (43.8 compiled\*) | 74.7 | 72.8 | 75.8 |
| sam2_hiera_large | 224.4 | 24.2 (30.2 compiled\*) | 76.0 | 74.6 | 79.8 |
\* Compile the model by setting `compile_image_encoder: True` in the config.
## Segment Anything Video Dataset
See [sav_dataset/README.md](sav_dataset/README.md) for details.
## License
The models are licensed under the [Apache 2.0 license](./LICENSE). Please refer to our research paper for more details on the models.
## Contributing
See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
## Contributors
The SAM 2 project was made possible with the help of many contributors (alphabetical):
Karen Bergan, Daniel Bolya, Alex Bosenberg, Kai Brown, Vispi Cassod, Christopher Chedeau, Ida Cheng, Luc Dahlin, Shoubhik Debnath, Rene Martinez Doehner, Grant Gardner, Sahir Gomez, Rishi Godugu, Baishan Guo, Caleb Ho, Andrew Huang, Somya Jain, Bob Kamma, Amanda Kallet, Jake Kinney, Alexander Kirillov, Shiva Koduvayur, Devansh Kukreja, Robert Kuo, Aohan Lin, Parth Malani, Jitendra Malik, Mallika Malhotra, Miguel Martin, Alexander Miller, Sasha Mitts, William Ngan, George Orlin, Joelle Pineau, Kate Saenko, Rodrick Shepard, Azita Shokrpour, David Soofian, Jonathan Torres, Jenny Truong, Sagar Vaze, Meng Wang, Claudette Ward, Pengchuan Zhang.
Third-party code: we use a GPU-based connected component algorithm adapted from [`cc_torch`](https://github.com/zsef123/Connected_components_PyTorch) (with its license in [`LICENSE_cctorch`](./LICENSE_cctorch)) as an optional post-processing step for the mask predictions.
## Citing SAM 2
If you use SAM 2 or the SA-V dataset in your research, please use the following BibTeX entry.
```bibtex
@article{ravi2024sam2,
title={SAM 2: Segment Anything in Images and Videos},
author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
journal={arXiv preprint arXiv:2408.00714},
url={https://arxiv.org/abs/2408.00714},
year={2024}
}
```
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"description": "# SAM 2: Segment Anything in Images and Videos\n\n**[AI at Meta, FAIR](https://ai.meta.com/research/)**\n\n[Nikhila Ravi](https://nikhilaravi.com/), [Valentin Gabeur](https://gabeur.github.io/), [Yuan-Ting Hu](https://scholar.google.com/citations?user=E8DVVYQAAAAJ&hl=en), [Ronghang Hu](https://ronghanghu.com/), [Chaitanya Ryali](https://scholar.google.com/citations?user=4LWx24UAAAAJ&hl=en), [Tengyu Ma](https://scholar.google.com/citations?user=VeTSl0wAAAAJ&hl=en), [Haitham Khedr](https://hkhedr.com/), [Roman R\u00e4dle](https://scholar.google.de/citations?user=Tpt57v0AAAAJ&hl=en), [Chloe Rolland](https://scholar.google.com/citations?hl=fr&user=n-SnMhoAAAAJ), [Laura Gustafson](https://scholar.google.com/citations?user=c8IpF9gAAAAJ&hl=en), [Eric Mintun](https://ericmintun.github.io/), [Junting Pan](https://junting.github.io/), [Kalyan Vasudev Alwala](https://scholar.google.co.in/citations?user=m34oaWEAAAAJ&hl=en), [Nicolas Carion](https://www.nicolascarion.com/), [Chao-Yuan Wu](https://chaoyuan.org/), [Ross Girshick](https://www.rossgirshick.info/), [Piotr Doll\u00e1r](https://pdollar.github.io/), [Christoph Feichtenhofer](https://feichtenhofer.github.io/)\n\n[[`Paper`](https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/)] [[`Project`](https://ai.meta.com/sam2)] [[`Demo`](https://sam2.metademolab.com/)] [[`Dataset`](https://ai.meta.com/datasets/segment-anything-video)] [[`Blog`](https://ai.meta.com/blog/segment-anything-2)] [[`BibTeX`](#citing-sam-2)]\n\n\n\n**Segment Anything Model 2 (SAM 2)** is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect [**our SA-V dataset**](https://ai.meta.com/datasets/segment-anything-video), the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains.\n\n\n\n## Installation\n\nSAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:\n\n```bash\ngit clone https://github.com/facebookresearch/segment-anything-2.git\n\ncd segment-anything-2 & pip install -e .\n```\nIf you are installing on Windows, it's strongly recommended to use [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) with Ubuntu.\n\nTo use the SAM 2 predictor and run the example notebooks, `jupyter` and `matplotlib` are required and can be installed by:\n\n```bash\npip install -e \".[demo]\"\n```\n\nNote:\n1. It's recommended to create a new Python environment via [Anaconda](https://www.anaconda.com/) for this installation and install PyTorch 2.3.1 (or higher) via `pip` following https://pytorch.org/. If you have a PyTorch version lower than 2.3.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using `pip`.\n2. The step above requires compiling a custom CUDA kernel with the `nvcc` compiler. If it isn't already available on your machine, please install the [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) with a version that matches your PyTorch CUDA version.\n3. If you see a message like `Failed to build the SAM 2 CUDA extension` during installation, you can ignore it and still use SAM 2 (some post-processing functionality may be limited, but it doesn't affect the results in most cases).\n\nPlease see [`INSTALL.md`](./INSTALL.md) for FAQs on potential issues and solutions.\n\n## Getting Started\n\n### Download Checkpoints\n\nFirst, we need to download a model checkpoint. All the model checkpoints can be downloaded by running:\n\n```bash\ncd checkpoints && \\\n./download_ckpts.sh && \\\ncd ..\n```\n\nor individually from:\n\n- [sam2_hiera_tiny.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt)\n- [sam2_hiera_small.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt)\n- [sam2_hiera_base_plus.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_base_plus.pt)\n- [sam2_hiera_large.pt](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt)\n\nThen SAM 2 can be used in a few lines as follows for image and video prediction.\n\n### Image prediction\n\nSAM 2 has all the capabilities of [SAM](https://github.com/facebookresearch/segment-anything) on static images, and we provide image prediction APIs that closely resemble SAM for image use cases. The `SAM2ImagePredictor` class has an easy interface for image prompting.\n\n```python\nimport torch\nfrom sam2.build_sam import build_sam2\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\n\ncheckpoint = \"./checkpoints/sam2_hiera_large.pt\"\nmodel_cfg = \"sam2_hiera_l.yaml\"\npredictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n predictor.set_image(<your_image>)\n masks, _, _ = predictor.predict(<input_prompts>)\n```\n\nPlease refer to the examples in [image_predictor_example.ipynb](./notebooks/image_predictor_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/segment-anything-2/blob/main/notebooks/image_predictor_example.ipynb)) for static image use cases.\n\nSAM 2 also supports automatic mask generation on images just like SAM. Please see [automatic_mask_generator_example.ipynb](./notebooks/automatic_mask_generator_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/segment-anything-2/blob/main/notebooks/automatic_mask_generator_example.ipynb)) for automatic mask generation in images.\n\n### Video prediction\n\nFor promptable segmentation and tracking in videos, we provide a video predictor with APIs for example to add prompts and propagate masklets throughout a video. SAM 2 supports video inference on multiple objects and uses an inference state to keep track of the interactions in each video.\n\n```python\nimport torch\nfrom sam2.build_sam import build_sam2_video_predictor\n\ncheckpoint = \"./checkpoints/sam2_hiera_large.pt\"\nmodel_cfg = \"sam2_hiera_l.yaml\"\npredictor = build_sam2_video_predictor(model_cfg, checkpoint)\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n state = predictor.init_state(<your_video>)\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):\n\n # propagate the prompts to get masklets throughout the video\n for frame_idx, object_ids, masks in predictor.propagate_in_video(state):\n ...\n```\n\nPlease refer to the examples in [video_predictor_example.ipynb](./notebooks/video_predictor_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/segment-anything-2/blob/main/notebooks/video_predictor_example.ipynb)) for details on how to add click or box prompts, make refinements, and track multiple objects in videos.\n\n## Load from \ud83e\udd17 Hugging Face\n\nAlternatively, models can also be loaded from [Hugging Face](https://huggingface.co/models?search=facebook/sam2) (requires `pip install huggingface_hub`).\n\nFor image prediction:\n\n```python\nimport torch\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\n\npredictor = SAM2ImagePredictor.from_pretrained(\"facebook/sam2-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n predictor.set_image(<your_image>)\n masks, _, _ = predictor.predict(<input_prompts>)\n```\n\nFor video prediction:\n\n```python\nimport torch\nfrom sam2.sam2_video_predictor import SAM2VideoPredictor\n\npredictor = SAM2VideoPredictor.from_pretrained(\"facebook/sam2-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n state = predictor.init_state(<your_video>)\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):\n\n # propagate the prompts to get masklets throughout the video\n for frame_idx, object_ids, masks in predictor.propagate_in_video(state):\n ...\n```\n\n## Model Description\n\n| **Model** | **Size (M)** | **Speed (FPS)** | **SA-V test (J&F)** | **MOSE val (J&F)** | **LVOS v2 (J&F)** |\n| :------------------: | :----------: | :--------------------: | :-----------------: | :----------------: | :---------------: |\n| sam2_hiera_tiny | 38.9 | 47.2 | 75.0 | 70.9 | 75.3 |\n| sam2_hiera_small | 46 | 43.3 (53.0 compiled\\*) | 74.9 | 71.5 | 76.4 |\n| sam2_hiera_base_plus | 80.8 | 34.8 (43.8 compiled\\*) | 74.7 | 72.8 | 75.8 |\n| sam2_hiera_large | 224.4 | 24.2 (30.2 compiled\\*) | 76.0 | 74.6 | 79.8 |\n\n\\* Compile the model by setting `compile_image_encoder: True` in the config.\n\n## Segment Anything Video Dataset\n\nSee [sav_dataset/README.md](sav_dataset/README.md) for details.\n\n## License\n\nThe models are licensed under the [Apache 2.0 license](./LICENSE). Please refer to our research paper for more details on the models.\n\n## Contributing\n\nSee [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).\n\n## Contributors\n\nThe SAM 2 project was made possible with the help of many contributors (alphabetical):\n\nKaren Bergan, Daniel Bolya, Alex Bosenberg, Kai Brown, Vispi Cassod, Christopher Chedeau, Ida Cheng, Luc Dahlin, Shoubhik Debnath, Rene Martinez Doehner, Grant Gardner, Sahir Gomez, Rishi Godugu, Baishan Guo, Caleb Ho, Andrew Huang, Somya Jain, Bob Kamma, Amanda Kallet, Jake Kinney, Alexander Kirillov, Shiva Koduvayur, Devansh Kukreja, Robert Kuo, Aohan Lin, Parth Malani, Jitendra Malik, Mallika Malhotra, Miguel Martin, Alexander Miller, Sasha Mitts, William Ngan, George Orlin, Joelle Pineau, Kate Saenko, Rodrick Shepard, Azita Shokrpour, David Soofian, Jonathan Torres, Jenny Truong, Sagar Vaze, Meng Wang, Claudette Ward, Pengchuan Zhang.\n\nThird-party code: we use a GPU-based connected component algorithm adapted from [`cc_torch`](https://github.com/zsef123/Connected_components_PyTorch) (with its license in [`LICENSE_cctorch`](./LICENSE_cctorch)) as an optional post-processing step for the mask predictions.\n\n## Citing SAM 2\n\nIf you use SAM 2 or the SA-V dataset in your research, please use the following BibTeX entry.\n\n```bibtex\n@article{ravi2024sam2,\n title={SAM 2: Segment Anything in Images and Videos},\n author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\\\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\\'a}r, Piotr and Feichtenhofer, Christoph},\n journal={arXiv preprint arXiv:2408.00714},\n url={https://arxiv.org/abs/2408.00714},\n year={2024}\n}\n```\n",
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