Add source model attribution and citation

d82f324 verified about 2 months ago

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license: apache-2.0
pipeline_tag: mask-generation
tags:
  - sam2
  - segment-anything
  - instance-segmentation
  - robotics
  - IB-Robot
  - ascend
  - edge-deployment
base_model:
  - facebook/sam2.1-hiera-tiny

Model Card for SAM2.1 Hiera Tiny (IB-Robot)

Segment Anything Model 2.1 (SAM2.1) with Hiera-Tiny backbone, packaged for the IB-Robot framework. Performs prompted segmentation: given an image and point/box prompts, generates instance masks.

Repository Structure

inference_manifest.json — deployment routing (schema v3)
assets/ — adapter config, torch weights (.pt)
artifacts/ascend_310b/ — encoder + decoder OM (batch=1)
artifacts/ascend_310p/ — encoder + decoder OM (batch=4)

Deployment Backends

Target	Backend	Runtime	Batch	Hardware
`ascend_310b`	ascend	ACL	1	Ascend 310B1
`ascend_310p`	ascend	ACL	4	Ascend 310P1
`torch_cpu`	torch	PyTorch	—	CPU
`torch_cuda`	torch	PyTorch	—	NVIDIA GPU

Each Ascend deployment runs a two-stage pipeline: encoder then decoder.

Encoder input: host.sam2.image float32 [N,3,1024,1024] NCHW Decoder outputs: host.sam2.mask_logits [N,1,256,256], host.sam2.iou_predictions [N,1], host.sam2.low_res_masks [N,1,256,256]

Source Model

This bundle's torch weights originate from the upstream SAM2.1 checkpoint:

HuggingFace: facebook/sam2.1-hiera-tiny
Direct checkpoint: https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt

The Ascend OM artifacts were converted from these torch weights. Download script: scripts/download_perception_models.sh.

Citation

@inproceedings{sam2,
  title     = {SAM 2: Segment Anything in Images and Videos},
  author    = {Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and others},
  booktitle = {arXiv preprint arXiv:2408.00714},
  year      = {2024}
}
@software{ib_robot,
  title   = {IB-Robot: Intelligence Boom Robot},
  url     = {https://gitcode.com/openeuler/IB_Robot},
  license = {Apache-2.0}
}