Transformers How to use robotflowlabs/sam2.1-hiera-small-int8 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-segmentation", model="robotflowlabs/sam2.1-hiera-small-int8") # Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("robotflowlabs/sam2.1-hiera-small-int8")
model = AutoModel.from_pretrained("robotflowlabs/sam2.1-hiera-small-int8", device_map="auto") sam2 How to use robotflowlabs/sam2.1-hiera-small-int8 with sam2:
# Use SAM2 with images
import torch
from sam2.sam2_image_predictor import SAM2ImagePredictor
predictor = SAM2ImagePredictor.from_pretrained(robotflowlabs/sam2.1-hiera-small-int8)
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(robotflowlabs/sam2.1-hiera-small-int8)
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):
... 
