Image Segmentation
Transformers
PyTorch
pixdlm
cvpr-2026
compute-transparency
reasoning-segmentation
uav
remote-sensing
vision-language
Instructions to use WhynotHug/PixDLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WhynotHug/PixDLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="WhynotHug/PixDLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WhynotHug/PixDLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model/llava/language_model/mpt/custom_embedding.py from WhynotHug/PixDLM: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/WhynotHug/PixDLM/resolve/main/model/llava/language_model/mpt/custom_embedding.py
- Command line
-
hf download hf://WhynotHug/PixDLM/model/llava/language_model/mpt/custom_embedding.py
-
curl -L -o custom_embedding.py https://huggingface.co/WhynotHug/PixDLM/resolve/main/model/llava/language_model/mpt/custom_embedding.py
308 Bytes
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| class SharedEmbedding(nn.Embedding): | |
| def forward(self, input: Tensor, unembed: bool = False) -> Tensor: | |
| if unembed: | |
| return F.linear(input, self.weight) | |
| return super().forward(input) | |