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
File size: 622 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | // Copyright (c) Facebook, Inc. and its affiliates.
#include <cuda_runtime_api.h>
namespace detectron2 {
int get_cudart_version() {
// Not a ROCM platform: Either HIP is not used, or
// it is used, but platform is not ROCM (i.e. it is CUDA)
#if !defined(__HIP_PLATFORM_AMD__)
return CUDART_VERSION;
#else
int version = 0;
#if HIP_VERSION_MAJOR != 0
// Create a convention similar to that of CUDA, as assumed by other
// parts of the code.
version = HIP_VERSION_MINOR;
version += (HIP_VERSION_MAJOR * 100);
#else
hipRuntimeGetVersion(&version);
#endif
return version;
#endif
}
} // namespace detectron2
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