Instructions to use meowmeowai/dual-explain-sam2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use meowmeowai/dual-explain-sam2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tkhangg0910/Merged-Dual-Explain-Llava-Stage_1-7b") model = PeftModel.from_pretrained(base_model, "meowmeowai/dual-explain-sam2") - sam2
How to use meowmeowai/dual-explain-sam2 with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(meowmeowai/dual-explain-sam2) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained(meowmeowai/dual-explain-sam2) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
Dual-Explain Stage Full โ SAM2
Final LoRA adapter and saved task modules for the SAM2 ablation.
Dependencies:
- Stage-1 base:
tkhangg0910/Merged-Dual-Explain-Llava-Stage_1-7b - Segmentation backbone:
facebook/sam2-hiera-large - Segmentation type:
sam2 - Classes: GCA, Koedam, MTA
- Evaluation mask threshold: 0.5
See PEFT_README.md for the generated PEFT metadata.
- Downloads last month
- 24
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support