README.md

---
license: other
tags:
- vision
- image-segmentation
datasets:
- coco
widget:
- src: http://images.cocodataset.org/val2017/000000039769.jpg
  example_title: Cats
- src: http://images.cocodataset.org/val2017/000000039770.jpg
  example_title: Castle
---

# Mask2Former for Semantic Segmentation

This repository contains the `Mask2Former` model fine-tuned for semantic segmentation tasks. The model can be used to predict segmentation masks on input images and is based on the `facebook/mask2former-swin-large-cityscapes-semantic` pre-trained model.

## Model Overview
Mask2Former is a general-purpose framework for mask prediction tasks, including:
- Semantic Segmentation
- Instance Segmentation
- Panoptic Segmentation

This version has been fine-tuned and optimized for semantic segmentation tasks. You can use it for tasks such as road scene understanding, autonomous driving, and other segmentation-related applications.

---

## How to Use the Model

You can use this model with the `transformers` library from Hugging Face. Below is an example to load the model, process an image, and visualize the output.

### Installation
First, ensure you have the required libraries installed:
```bash
pip install transformers torch torchvision pillow matplotlib


```

### How to use

Here is how to use this model:
```
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
from PIL import Image
import torch
import matplotlib.pyplot as plt

# Load the processor and model
model_name = "saninmohammedn/mask2former-deployment"
processor = AutoImageProcessor.from_pretrained(model_name)
model = Mask2FormerForUniversalSegmentation.from_pretrained(model_name)

# Load an input image
image_path = "your_image.jpg"  # Replace with your image path
image = Image.open(image_path).convert("RGB")

# Prepare the image for the model
inputs = processor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)

# Post-process the predicted segmentation map
predicted_map = processor.post_process_semantic_segmentation(
    outputs, target_sizes=[image.size[::-1]]
)[0].cpu().numpy()

# Visualize the input and predicted segmentation map
plt.figure(figsize=(10, 5))

# Display original image
plt.subplot(1, 2, 1)
plt.imshow(image)
plt.title("Original Image")
plt.axis("off")

# Display predicted segmentation map
plt.subplot(1, 2, 2)
plt.imshow(predicted_map, cmap="jet")
plt.title("Predicted Segmentation Map")
plt.axis("off")

plt.tight_layout()
plt.show()

```