Pros of segment-anything

• Highly versatile and can handle a wide range of segmentation tasks
• Backed by Meta AI, with extensive documentation and community support
• Offers pre-trained models for immediate use

Cons of segment-anything

• Computationally intensive, requiring significant resources for real-time applications
• Large model size, which can be challenging for deployment on edge devices
• May be overkill for simpler segmentation tasks

Code Comparison

segment-anything:

from segment_anything import sam_model_registry, SamPredictor

sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth")
predictor = SamPredictor(sam)
predictor.set_image(image)
masks, _, _ = predictor.predict(point_coords=input_point, point_labels=input_label)

EfficientSAM:

from efficient_sam.build_efficient_sam import build_efficient_sam
from efficient_sam.efficient_sam import SamPredictor

model = build_efficient_sam(checkpoint="efficient_sam_s.pt")
predictor = SamPredictor(model)
predictor.set_image(image)
masks, _, _ = predictor.predict(point_coords=input_point, point_labels=input_label)

Pros of Grounded-Segment-Anything

• Integrates grounding DINO for more accurate object detection and segmentation
• Supports text-to-mask generation, enhancing versatility in various applications
• Offers a wider range of pre-trained models and datasets

Cons of Grounded-Segment-Anything

• Higher computational requirements due to additional components
• More complex setup and installation process
• Potentially slower inference time compared to EfficientSAM

Code Comparison

EfficientSAM:

from efficient_sam import sam_model_registry, SamAutomaticMaskGenerator

sam = sam_model_registry["efficient_sam_s"](checkpoint="path/to/checkpoint")
mask_generator = SamAutomaticMaskGenerator(sam)
masks = mask_generator.generate(image)

Grounded-Segment-Anything:

from segment_anything import sam_model_registry, SamPredictor
from groundingdino.util.inference import load_model, load_image, predict

sam = sam_model_registry["vit_h"](checkpoint="path/to/sam_checkpoint")
grounding_dino_model = load_model("path/to/groundingdino_checkpoint")
predictor = SamPredictor(sam)
boxes, logits, phrases = predict(grounding_dino_model, image, text_prompt)
masks, _, _ = predictor.predict(point_coords=None, point_labels=None, box=boxes[0])

Pros of MobileSAM

• Significantly faster inference speed, making it more suitable for mobile and real-time applications
• Smaller model size, requiring less storage and memory
• Maintains comparable performance to the original SAM model

Cons of MobileSAM

• Slightly lower accuracy compared to EfficientSAM in some scenarios
• Less flexibility in terms of input image sizes and resolutions

Code Comparison

MobileSAM:

from mobile_sam import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor

sam = sam_model_registry["vit_t"](checkpoint="mobile_sam.pt")
mask_generator = SamAutomaticMaskGenerator(sam)
masks = mask_generator.generate(image)

EfficientSAM:

from efficient_sam import build_efficient_sam, SamAutomaticMaskGenerator

model = build_efficient_sam(checkpoint="efficient_sam.pt")
mask_generator = SamAutomaticMaskGenerator(model)
masks = mask_generator.generate(image)

Both repositories aim to improve the efficiency of the Segment Anything Model (SAM). MobileSAM focuses on speed and mobile deployment, while EfficientSAM aims for a balance between efficiency and accuracy. The code usage is similar, with minor differences in model initialization and import statements.

Pros of sam-hq

• Higher quality segmentation masks with more precise boundaries
• Better performance on high-resolution images
• Improved handling of complex scenes with multiple objects

Cons of sam-hq

• Potentially slower inference time due to higher complexity
• May require more computational resources
• Limited to specific use cases where high-quality segmentation is critical

Code Comparison

sam-hq:

predictor = SamPredictor(sam)
predictor.set_image(image)
masks, _, _ = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=True,
)

EfficientSAM:

sam = sam_model_registry["vit_t"](checkpoint="weights/mobile_sam.pt")
predictor = SamPredictor(sam)
predictor.set_image(image)
masks, _, _ = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
)

The main difference in the code is that EfficientSAM uses a more lightweight model ("vit_t") and doesn't have the multimask_output parameter, potentially resulting in faster but less detailed predictions compared to sam-hq.

Pros of Anylabeling

• More comprehensive labeling tool with support for various annotation types (bounding boxes, polygons, keypoints, etc.)
• User-friendly GUI interface for easier annotation and project management
• Supports multiple AI models and integrates with popular frameworks like YOLO and Segment Anything

Cons of Anylabeling

• Larger codebase and potentially more complex setup compared to EfficientSAM
• May have higher system requirements due to its extensive features
• Less focused on a specific task, which could impact performance in specialized use cases

Code Comparison

EfficientSAM:

sam = sam_model_registry[args.model_type](checkpoint=args.checkpoint)
predictor = SamPredictor(sam)
masks, _, _ = predictor.predict(point_coords=input_point, point_labels=input_label, multimask_output=True)

Anylabeling:

self.sam_controller = SamController()
self.sam_controller.load_sam_model(model_path, model_type, device)
masks, scores, logits = self.sam_controller.predict(image, input_point, input_label)

Both repositories use similar approaches for loading and predicting with SAM models, but Anylabeling wraps the functionality in a controller class for better integration with its GUI-based system.