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--- |
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license: apache-2.0 |
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tags: |
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- mask2former |
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- instance-segmentation |
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- panoptic-segmentation |
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- semantic-segmentation |
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- image-segmentation |
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datasets: |
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- custom |
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pipeline_tag: image-segmentation |
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--- |
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# Mask2Former for Segmentation |
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This model is fine-tuned to detect and segment regions across 3 classes. |
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## Model description |
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This is a Mask2Former model fine-tuned on a custom dataset with polygon annotations in COCO format. It has 3 classes: |
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- Background (ID: 0) |
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- Normal (ID: 1) |
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- Abnormal (ID: 2) |
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## Intended uses & limitations |
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This model is intended for universal segmentation tasks to identify the specified region types in images. Mask2Former supports instance, semantic, and panoptic segmentation. |
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### How to use in CVAT |
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1. In CVAT, go to Models → Add Model |
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2. Select Hugging Face as the source |
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3. Enter the model path: "{your-username}/mask2former-segmentation" |
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4. Configure the appropriate mapping for your labels |
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### Usage in Python |
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```python |
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from transformers import Mask2FormerForUniversalSegmentation, Mask2FormerImageProcessor |
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import torch |
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from PIL import Image |
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# Load model and processor |
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model = Mask2FormerForUniversalSegmentation.from_pretrained("{your-username}/mask2former-segmentation") |
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processor = Mask2FormerImageProcessor.from_pretrained("{your-username}/mask2former-segmentation") |
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# Prepare image |
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image = Image.open("your_image.jpg") |
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inputs = processor(images=image, return_tensors="pt") |
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# Make prediction |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Process outputs for visualization |
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# (see example code in model repository) |
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``` |
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