Hugging Face's logo

flaviagiammarino
/
medsam-vit-base

Mask Generation
Transformers
PyTorch
TensorFlow
sam
medical
vision
Inference Endpoints
Model card Files Community
1
medsam-vit-base / README.md
flaviagiammarino's picture
flaviagiammarino
Update README.md
8a6fc7f
preview code
|
raw
history blame
No virus
2.61 kB
metadata
license: apache-2.0
tags:
  - medical
  - vision

Model Card for MedSAM

MedSAM is a fine-tuned version of SAM for the medical domain.

Model Description

Usage 

import requests
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from transformers import SamModel, SamProcessor

device = "cuda" if torch.cuda.is_available() else "cpu"

model = SamModel.from_pretrained("flaviagiammarino/medsam-vit-base").to(device)
processor = SamProcessor.from_pretrained("flaviagiammarino/medsam-vit-base")

img_url = "https://raw.githubusercontent.com/bowang-lab/MedSAM/main/assets/img_demo.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_boxes = [95., 255., 190., 350.]

inputs = processor(raw_image, input_boxes=[[input_boxes]], return_tensors="pt").to(device)
outputs = model(**inputs, multimask_output=False)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())

def show_mask(mask, ax, random_color):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([251/255, 252/255, 30/255, 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="blue", facecolor=(0, 0, 0, 0), lw=2))

fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(np.array(raw_image))
show_box(input_boxes, ax[0])
ax[0].set_title("Input Image and Bounding Box")
ax[0].axis("off")
ax[1].imshow(np.array(raw_image))
show_mask(masks[0], ax=ax[1], random_color=False)
show_box(input_boxes, ax[1])
ax[1].set_title("MedSAM Segmentation")
ax[1].axis("off")
plt.show()

results

Additional Information

Licensing Information

The authors have released the model code and pre-trained checkpoints under the Apache License 2.0.

Citation Information 

@article{ma2023segment,
  title={Segment anything in medical images},
  author={Ma, Jun and Wang, Bo},
  journal={arXiv preprint arXiv:2304.12306},
  year={2023}
}