SAM2 Household Fine-tuned Checkpoint
Fine-tuned facebook/sam2.1-hiera-small for household object masks with bbox prompts.
- Base model:
facebook/sam2.1-hiera-small - Base revision:
ee5bba1d82bb8749febdf90f45e84b687142ba03 - Dataset source:
COCO 2017 instance segmentation - Dataset revision:
annotations_trainval2017 - Split manifest SHA-256:
74dfd6666397ec5398d0757de0b852641a7afc39c6f4ef1a652351653b54d2da - Training samples: 15000
- Validation samples: 1000
- Test samples: 895
- Steps: 350
- Batch size: 8
- Best validation IoU: 0.782299
- Best validation Dice: -1.0
- Production output mode: multimask with predicted-IoU candidate selection
- Test selected-candidate IoU: 0.800603
- Test oracle-candidate IoU: 0.827027
- Test selection regret: 0.026425
- Test IoU-head MAE: 0.079622
- W&B: https://wandb.ai/sshibinthomass-tu-dortmund/household-object-finetuning/runs/5uqfdkuu
- Modal app:
sam2-household-modal-train-v3 - Hub repo:
sshibinthomass/sam2-household-accuracy-v3-20260710-175456
Usage
from transformers import Sam2Model, Sam2Processor
processor = Sam2Processor.from_pretrained("sshibinthomass/sam2-household-accuracy-v3-20260710-175456")
model = Sam2Model.from_pretrained("sshibinthomass/sam2-household-accuracy-v3-20260710-175456")
outputs = model(**processor(images=image, input_boxes=[[box]], return_tensors="pt"), multimask_output=True)
best_mask_index = outputs.iou_scores[:, 0].argmax(dim=-1)