Initilize the `SAM`/`SAM2` predictor without inferencing

Kallinteris-Andreas · July 31, 2025, 2:02am

How to initialize SAM predictor without running inference to dummy images, to save time

from ultralytics import SAM
            
# Initialize
self.model = SAM("sam2tiny")
self.model()  ## note this prints:
"""
WARNING ⚠️ 'source' is missing. Using 'source=/home/master-andreas/my_proj/test_env/lib/python3.11/site-packages/ultralytics/assets'.

image 1/2 /home/master-andreas/my_proj/test_env/lib/python3.11/site-packages/ultralytics/assets/bus.jpg: 1024x1024 1 0, 1 1, 1 2, 1 3, 1 4, 1 5, 1 6, 1 7, 1 8, 1 9, 1 10, 1 11, 1 12, 9691.9ms
image 2/2 /home/master-andreas/my_proj/test_env/lib/python3.11/site-packages/ultralytics/assets/zidane.jpg: 1024x1024 1 0, 1 1, 1 2, 9208.4ms
"""

# Inference
results = self.model.predictor(image, points=[[point]], imgsz=size, multimask_output=True)

The reason why i want to initialize the predictor is: because i want multimask_output=True Ambigueous promt with SAM, return top 3 Masks

Toxite · July 31, 2025, 7:58pm

You can check this,:

github.com/ultralytics/ultralytics

Optimizing SAM Inference for High-Resolution Image Segmentation with Hundreds of Prompts

opened 07:55AM - 23 Jan 25 UTC

closed 01:05AM - 14 Feb 25 UTC

chickencoins

enhancement question segment

### Search before asking - [x] I have searched the Ultralytics YOLO [issues](ht…tps://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions. ### Question # Optimizing SAM Inference for High-Resolution Image Segmentation with Hundreds of Prompts ## **Description** I'm currently working on segmenting objects within high-resolution images using SAM 2.1 and MobileSAM. My workflow involves the following steps: 1. **Bounding Box Detection:** - I utilize another model to perform bounding box (bbox) detection. - A single image typically contains **hundreds of objects**, resulting in **hundreds of bbox annotations**. 2. **Segmentation with SAM:** - Mainly using bbox prompts, I employ SAM to segment each detected object. - **Challenge:** While SAM's inference time is relatively fast, I followed the method described in [Ultralytics SAM Documentation](https://docs.ultralytics.com/ko/models/sam-2/) (results = model("path/to/image.jpg", bboxes=[100, 100, 200, 200])). As a result, there are **hundreds of high-resolution image inputs** and **repeated image encodings**, which cause **significant time delays**. ## **Problem Statement** My primary goal is to efficiently segment hundreds of objects in a single high-resolution image. Specifically: - **Multiple Prompts Handling:** - For a single image, I need to input **hundreds of bbox or point prompts for different objects**. - **Performance Bottleneck:** - Current approaches cause significant time lags due to hundreds of high-resolution image inputs and iterative image encoding, although there is only one type of input image. - **Segmentation Accuracy:** - If you use whole image segmentation without specific prompts, objects are not accurately distinguished, so you must use bbox prompts. ## **Desired Outcome** To optimize the segmentation process, I aim to: 1. **Batch Processing of Prompts:** - If that's possibleI, i want input all (or a large batch of) bbox or point prompts **simultaneously** to minimize processing time. 2. **Single Image Encoding:** - **image encoding step is performed only once**, even if prompts are processed sequentially. ## **Request for Assistance** I'm seeking guidance or potential solutions to address the following: - **Efficient Prompt Handling:** - How can I input hundreds of prompts for a single high-resolution image without incurring significant processing delays? - **Optimizing Image Encoding:** - Is there a way to reuse image encoding from multiple prompt inputs, or to pre-encode and enter it when needed? ## **Additional Information** - **Tools & Versions:** - SAM 2.1 / MobileSAM - Ultralytics' SAM implementation - **Image Specifications:** - Ultra-high resolution (>10MB per image) - Hundreds of objects per image I would like to know if an official feature for this is provided, or if you have any reference materials, insights, or suggestions for similar implementations, I would greatly appreciate it. Thank you for your support!

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Initilize the `SAM`/`SAM2` predictor without inferencing

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