SAM V1,V2,V3
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# Use SAM2 with videos
import torch
from sam2.sam2_video_predictor import SAM2VideoPredictor
predictor = SAM2VideoPredictor.from_pretrained(kerasformers/sam2_hiera_base_plus)
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):
...sam2_hiera_base_plus (Keras 3)
Pure-Keras 3 weights for kerasformers, mirrored from the source. License: apache-2.0.
from kerasformers.models.sam2 import SAM2PromptableSegment
model = SAM2PromptableSegment.from_weights("sam2_hiera_base_plus")
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# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(kerasformers/sam2_hiera_base_plus) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)