| | --- |
| | license: apache-2.0 |
| | tags: |
| | - mask2former |
| | - instance-segmentation |
| | - panoptic-segmentation |
| | - semantic-segmentation |
| | - image-segmentation |
| | datasets: |
| | - custom |
| | pipeline_tag: image-segmentation |
| | --- |
| | |
| | # 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 |
| |
|
| | ```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) |
| | ``` |
| |
|
