đ Model Card for Segment Anything Model in High Quality (SAM-HQ)
SAM-HQ (Segment Anything in High Quality) is an enhanced version of the Segment Anything Model (SAM). It can generate higher - quality object masks from input prompts like points or boxes. While SAM was trained on a large dataset, its mask prediction quality has limitations, especially for objects with complex structures. SAM - HQ addresses these issues with minimal additional parameters and computation cost.
đ Quick Start
SAM-HQ is an advanced model for generating high - quality segmentation masks. It builds on the original SAM architecture and can be used in various scenarios such as prompted - mask - generation and automatic - mask - generation.
⨠Features
- High - Quality Output Token: A learnable token in the mask decoder, trained to predict high - quality masks.
- Global - local Feature Fusion: Fuses features from different stages of ViT for more accurate segmentation, combining high - level semantic context and low - level boundary information.
- Improved Mask Quality: Produces better masks, especially for objects with complex boundaries and thin structures, while maintaining SAM's original promptable design, efficiency, and zero - shot generalizability.
đĻ Installation
The README does not provide specific installation steps, so this section is skipped.
đģ Usage Examples
Basic Usage
Prompted - Mask - Generation
from PIL import Image
import requests
from transformers import SamHQModel, SamHQProcessor
model = SamHQModel.from_pretrained("syscv-community/sam-hq-vit-huge")
processor = SamHQProcessor.from_pretrained("syscv-community/sam-hq-vit-huge")
img_url = "https://raw.githubusercontent.com/SysCV/sam-hq/refs/heads/main/demo/input_imgs/example1.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_boxes = [[[306, 132, 925, 893]]]
inputs = processor(raw_image, input_boxes=input_boxes, return_tensors="pt").to("cuda")
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
scores = outputs.iou_scores
Automatic - Mask - Generation
from transformers import pipeline
generator = pipeline("mask-generation", model="syscv-community/sam-hq-vit-huge", device=0, points_per_batch=256)
image_url = "https://raw.githubusercontent.com/SysCV/sam-hq/refs/heads/main/demo/input_imgs/example1.png"
outputs = generator(image_url, points_per_batch=256)
Advanced Usage
Complete Example with Visualization
import numpy as np
import matplotlib.pyplot as plt
def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
color = np.array([30/255, 144/255, 255/255, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
def show_box(box, ax):
x0, y0 = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
def show_boxes_on_image(raw_image, boxes):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
for box in boxes:
show_box(box, plt.gca())
plt.axis('on')
plt.show()
def show_points_on_image(raw_image, input_points, input_labels=None):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
input_points = np.array(input_points)
if input_labels is None:
labels = np.ones_like(input_points[:, 0])
else:
labels = np.array(input_labels)
show_points(input_points, labels, plt.gca())
plt.axis('on')
plt.show()
def show_points_and_boxes_on_image(raw_image, boxes, input_points, input_labels=None):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
input_points = np.array(input_points)
if input_labels is None:
labels = np.ones_like(input_points[:, 0])
else:
labels = np.array(input_labels)
show_points(input_points, labels, plt.gca())
for box in boxes:
show_box(box, plt.gca())
plt.axis('on')
plt.show()
def show_points_and_boxes_on_image(raw_image, boxes, input_points, input_labels=None):
plt.figure(figsize=(10,10))
plt.imshow(raw_image)
input_points = np.array(input_points)
if input_labels is None:
labels = np.ones_like(input_points[:, 0])
else:
labels = np.array(input_labels)
show_points(input_points, labels, plt.gca())
for box in boxes:
show_box(box, plt.gca())
plt.axis('on')
plt.show()
def show_points(coords, labels, ax, marker_size=375):
pos_points = coords[labels==1]
neg_points = coords[labels==0]
ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
def show_masks_on_image(raw_image, masks, scores):
if len(masks.shape) == 4:
masks = masks.squeeze()
if scores.shape[0] == 1:
scores = scores.squeeze()
nb_predictions = scores.shape[-1]
fig, axes = plt.subplots(1, nb_predictions, figsize=(15, 15))
for i, (mask, score) in enumerate(zip(masks, scores)):
mask = mask.cpu().detach()
axes[i].imshow(np.array(raw_image))
show_mask(mask, axes[i])
axes[i].title.set_text(f"Mask {i+1}, Score: {score.item():.3f}")
axes[i].axis("off")
plt.show()
def show_masks_on_single_image(raw_image, masks, scores):
if len(masks.shape) == 4:
masks = masks.squeeze()
if scores.shape[0] == 1:
scores = scores.squeeze()
image_np = np.array(raw_image)
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(image_np)
for i, (mask, score) in enumerate(zip(masks, scores)):
mask = mask.cpu().detach().numpy()
show_mask(mask, ax)
ax.set_title(f"Overlayed Masks with Scores")
ax.axis("off")
plt.show()
import torch
from transformers import SamHQModel, SamHQProcessor
device = "cuda" if torch.cuda.is_available() else "cpu"
model = SamHQModel.from_pretrained("syscv-community/sam-hq-vit-huge").to(device)
processor = SamHQProcessor.from_pretrained("syscv-community/sam-hq-vit-huge")
from PIL import Image
import requests
img_url = "https://raw.githubusercontent.com/SysCV/sam-hq/refs/heads/main/demo/input_imgs/example1.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
plt.imshow(raw_image)
inputs = processor(raw_image, return_tensors="pt").to(device)
image_embeddings, intermediate_embeddings = model.get_image_embeddings(inputs["pixel_values"])
input_boxes = [[[306, 132, 925, 893]]]
show_boxes_on_image(raw_image, input_boxes[0])
inputs.pop("pixel_values", None)
inputs.update({"image_embeddings": image_embeddings})
inputs.update({"intermediate_embeddings": intermediate_embeddings})
with torch.no_grad():
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
scores = outputs.iou_scores
show_masks_on_single_image(raw_image, masks[0], scores)
show_masks_on_image(raw_image, masks[0], scores)
đ Documentation
Model Details
SAM-HQ builds on the original SAM architecture with two key innovations while keeping SAM's pretrained weights:
- High - Quality Output Token: A learnable token in the mask decoder, responsible for predicting high - quality masks. It and its associated MLP layers are specifically trained for accurate segmentation masks.
- Global - local Feature Fusion: Instead of only using the HQ - Output Token on mask - decoder features, SAM - HQ first fuses these features with early and final ViT features to improve mask details.
SAM-HQ was trained on a carefully curated dataset of 44K fine - grained masks (HQSeg - 44K). The training takes only 4 hours on 8 GPUs, with less than 0.5% additional parameters compared to the original SAM model.
The model has been evaluated on 10 diverse segmentation datasets. Results show that SAM - HQ can produce better masks than the original SAM model while maintaining zero - shot generalization capabilities.
Citation
@misc{ke2023segmenthighquality,
title={Segment Anything in High Quality},
author={Lei Ke and Mingqiao Ye and Martin Danelljan and Yifan Liu and Yu - Wing Tai and Chi - Keung Tang and Fisher Yu},
year={2023},
eprint={2306.01567},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2306.01567},
}
đ License
The model is licensed under the apache - 2.0
license.