Image Segmentation
Transformers
Safetensors
sam2
instance-segmentation
panoptic-segmentation
semantic-segmentation
zero-shot
open-vocabulary
beit3
fiftyone
Instructions to use Voxel51/openworld-sam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Voxel51/openworld-sam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Voxel51/openworld-sam")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Voxel51/openworld-sam", dtype="auto") - sam2
How to use Voxel51/openworld-sam with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(Voxel51/openworld-sam) 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(Voxel51/openworld-sam) 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
| DATASETS: | |
| TRAIN: ("coco_2017_train",) | |
| TEST: ("coco_2017_val",) | |
| SOLVER: | |
| IMS_PER_BATCH: 8 | |
| BASE_LR: 0.0001 | |
| STEPS: (135000,150000) | |
| MAX_ITER: 500000 | |
| CHECKPOINT_PERIOD: 50000 | |
| WARMUP_FACTOR: 1.0 | |
| WARMUP_ITERS: 10 | |
| WEIGHT_DECAY: 0.1 | |
| OPTIMIZER: "ADAMW" | |
| BACKBONE_MULTIPLIER: 0.1 | |
| CLIP_GRADIENTS: | |
| ENABLED: True | |
| CLIP_TYPE: "full_model" | |
| CLIP_VALUE: 0.01 | |
| NORM_TYPE: 2.0 | |
| AMP: | |
| ENABLED: True | |
| INPUT: | |
| IMAGE_SIZE: 1024 | |
| FORMAT: "RGB" | |
| DATASET_MAPPER_NAME: "coco_instance_lsj" | |
| TEST: | |
| EVAL_PERIOD: 5000 | |
| # EVAL_FLAG: 1 | |
| DATALOADER: | |
| FILTER_EMPTY_ANNOTATIONS: True | |
| NUM_WORKERS: 8 | |
| VERSION: 2 | |