| --- |
| 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](https://gitcode.com/openeuler/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](https://huggingface.co/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} |
| } |
| |
