Instructions to use MingzhenL/vessa-2d-ssl-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use MingzhenL/vessa-2d-ssl-bundle with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("MingzhenL/vessa-2d-ssl-bundle") 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("MingzhenL/vessa-2d-ssl-bundle") 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
File size: 611 Bytes
01857e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | #!/bin/bash
#SBATCH -A p33139
#SBATCH -p gengpu
#SBATCH --gres=gpu:a100:1
#SBATCH -N 1 -n 1
#SBATCH -t 48:00:00
#SBATCH --mem=80G
#SBATCH -o /projects/p32685/VESSA_data_preprocess/logs/total_reduced_%j.out
#SBATCH -e /projects/p32685/VESSA_data_preprocess/logs/total_reduced_%j.err
source /projects/p32685/miniconda3/etc/profile.d/conda.sh
conda activate msd_preprocess
cd /projects/p32685/VESSA_data_preprocess
mkdir -p /projects/p32685/VESSA_data_preprocess/logs
# 处理 TotalSegmentator_reduced 和 TotalSegmentatorMRI_reduced
python -u process_total_reduced.py --start_from 1 --end_at 2 --device cuda:0
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