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openEuler/grounding_dino_swint_seq8_1280x720
grounding_dino_swint_seq8_1280x720 is a object detection model from openEuler. Use it when you need objects located in an image. The card lists the license as apache-2.0.
Grounding DINO with Swin-Tiny backbone and the OGC checkpoint (groundingdinoswintogc.pth, sha256 3b3ca2563c77c69f651d7bd133e97139c186df06231157a64c507099c52bc799) for text-conditioned zero-shot object detection, packa…
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Updated Sep 22, 2026
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.pth694 MB · 60%
From the Hugging Face model README
Grounding DINO with Swin-Tiny backbone and the OGC checkpoint
(groundingdino_swint_ogc.pth, sha256
3b3ca2563c77c69f651d7bd133e97139c186df06231157a64c507099c52bc799) for
text-conditioned zero-shot object detection, packaged for the
IB-Robot framework with three
deployments sharing one raw contract.
| deployment | backend | artifacts | notes |
|---|---|---|---|
ascend_310p | Ascend ACL (Ascend310P1) | 12 OM modules | fixed 720x1280 / seq8 split graph (text/vision/encoder/proposal/decoder/head) |
torch_cuda | PyTorch CUDA | — (weights in assets/) | host-side, ~0.16 s/inference on RTX 3090 |
torch_cpu | PyTorch CPU | — (weights in assets/) | host-side fallback (~3.7 s/inference) |
Shared raw contract: tensor_model/grounding_dino/detect with fixed-shape
inputs (image [1,3,720,1280], BERT seq8 token tensors, encoder_tgt
constant for the compiled graph) and outputs
(pred_logits [1,900,256], pred_boxes [1,900,4]). The Torch deployments
ignore encoder_tgt (the model owns its query embeddings) and saturate the
ContrastiveEmbed -inf padding columns to -50 to match the compiled artifacts'
finite-value contract.
All three deployments use the byte-identical IDEA-Research official
groundingdino_swint_ogc.pth (same sha256 as the local
grounded_sam2_swint_ogc Torch bundle); the 310P OM modules were converted
from it.
inference_manifest.json — deployment routing (schema v3)inference_integrity.json — per-artifact digestsassets/adapter.json — identity + torch_module_loaderassets/groundingdino_swint_ogc.pth — Torch weights (693 MB)assets/bert-base-uncased/vocab.txt, assets/encoder_tgt.npy — shared contract assetsartifacts/ascend_310p/*.om — 12 compiled modulesHost (Torch, via the unified inference runtime):
ros2 run inference_service model_service_node --ros-args \
-p bundle_path:=models/grounding_dino_swint_seq8_1280x720 \
-p deployment:=torch_cuda \
-p adapter_class:=perception_service.model_service_plugins:GroundingDINORawDetectPlugin \
-p service_type:=ibrobot_msgs/srv/GroundingDetect \
-p service_endpoint:=/perception/grounding_detect
Board (Ascend 310P): select the ascend_310p deployment (8-role
device-linked execution through the unified inference runtime).
Code and packaging: Apache-2.0. GroundingDINO weights follow the IDEA-Research upstream license (Apache-2.0).