Image Segmentation
Transformers
PyTorch
English
garment-mask-generation
image-inpainting
fashion
garment-mask
densepose
human-parsing
Instructions to use Ekliipce/wearit-garment-mask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ekliipce/wearit-garment-mask with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Ekliipce/wearit-garment-mask")# Load model directly from transformers import GarmentMaskPipeline model = GarmentMaskPipeline.from_pretrained("Ekliipce/wearit-garment-mask", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| // Copyright (c) Facebook, Inc. and its affiliates. | |
| namespace detectron2 { | |
| at::Tensor box_iou_rotated_cpu( | |
| const at::Tensor& boxes1, | |
| const at::Tensor& boxes2); | |
| at::Tensor box_iou_rotated_cuda( | |
| const at::Tensor& boxes1, | |
| const at::Tensor& boxes2); | |
| // Interface for Python | |
| // inline is needed to prevent multiple function definitions when this header is | |
| // included by different cpps | |
| inline at::Tensor box_iou_rotated( | |
| const at::Tensor& boxes1, | |
| const at::Tensor& boxes2) { | |
| assert(boxes1.device().is_cuda() == boxes2.device().is_cuda()); | |
| if (boxes1.device().is_cuda()) { | |
| return box_iou_rotated_cuda(boxes1.contiguous(), boxes2.contiguous()); | |
| AT_ERROR("Detectron2 is not compiled with GPU support!"); | |
| } | |
| return box_iou_rotated_cpu(boxes1.contiguous(), boxes2.contiguous()); | |
| } | |
| } // namespace detectron2 | |