Dreamy0/mask2former-segmentation-all-data

Mask2Former for Segmentation

This model is fine-tuned to detect and segment regions across 3 classes.

Model description

This is a Mask2Former model fine-tuned on a custom dataset with polygon annotations in COCO format. It has 3 classes:

• Background (ID: 0)
• Normal (ID: 1)
• Abnormal (ID: 2)

Intended uses & limitations

This model is intended for universal segmentation tasks to identify the specified region types in images. Mask2Former supports instance, semantic, and panoptic segmentation.

How to use in CVAT

1. In CVAT, go to Models → Add Model
2. Select Hugging Face as the source
3. Enter the model path: "{your-username}/mask2former-segmentation"
4. Configure the appropriate mapping for your labels

Usage in Python

from transformers import Mask2FormerForUniversalSegmentation, Mask2FormerImageProcessor
import torch
from PIL import Image

# Load model and processor
model = Mask2FormerForUniversalSegmentation.from_pretrained("{your-username}/mask2former-segmentation")
processor = Mask2FormerImageProcessor.from_pretrained("{your-username}/mask2former-segmentation")

# Prepare image
image = Image.open("your_image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)

# Process outputs for visualization
# (see example code in model repository)