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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WhynotHug/PixDLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import AutoConfig | |
| def auto_upgrade(config): | |
| cfg = AutoConfig.from_pretrained(config) | |
| if "llava" in config and "llava" not in cfg.model_type: | |
| assert cfg.model_type == "llama" | |
| print( | |
| "You are using newer LLaVA code base, while the checkpoint of v0 is from older code base." | |
| ) | |
| print( | |
| "You must upgrade the checkpoint to the new code base (this can be done automatically)." | |
| ) | |
| confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]") | |
| if confirm.lower() in ["y", "yes"]: | |
| print("Upgrading checkpoint...") | |
| assert len(cfg.architectures) == 1 | |
| setattr(cfg.__class__, "model_type", "llava") | |
| cfg.architectures[0] = "LlavaLlamaForCausalLM" | |
| cfg.save_pretrained(config) | |
| print("Checkpoint upgraded.") | |
| else: | |
| print("Checkpoint upgrade aborted.") | |
| exit(1) | |