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Browse files- .gitattributes +1 -0
- README.md +70 -0
- config.json +24 -0
- rtmdet_inst_tiny.bin +3 -0
- rtmdet_inst_tiny.xml +0 -0
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README.md
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---
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license: apache-2.0
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tags:
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- image-segmentation
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- instance-segmentation
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- vision
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---
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# rtmdet_inst_tiny-fp16-ov
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- Model creator: [Geti™](https://github.com/open-edge-platform/geti)
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- Original model: [RTMDet Instance Tiny](https://github.com/open-edge-platform/geti)
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## Description
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This is a [Geti™](https://github.com/open-edge-platform/geti) version of [RTMDet Instance Tiny](https://github.com/open-edge-platform/geti) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16.
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To fine-tune your model with a custom dataset, you can use Geti™ to annotate data, perform fine-tuning, and export the resulting model.
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## Compatibility
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The provided OpenVINO™ IR model is compatible with:
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- OpenVINO version 2026.1.0 and higher
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- Model API 0.4.0 and higher
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## Running Model Inference with [Model API](https://github.com/open-edge-platform/model_api)
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1. Install required packages:
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```sh
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pip install openvino-model-api[huggingface]
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```
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<!-- markdownlint-disable MD029 -->
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2. Run model inference:
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```python
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import cv2
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from model_api.models import Model
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from model_api.visualizer import Visualizer
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# 1. Load model
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model = Model.from_pretrained("OpenVINO/rtmdet_inst_tiny-fp16-ov")
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# 2. Load image
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image = cv2.imread("image.jpg")
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# 3. Run inference
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result = model(image)
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# 4. Visualize and save results
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vis = Visualizer().render(image, result)
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cv2.imwrite("output.jpg", vis)
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```
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For more examples and possible optimizations, refer to the [Model API Documentation](https://open-edge-platform.github.io/model_api/latest/).
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## Limitations
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Check the original [model documentation](https://github.com/open-edge-platform/geti) for limitations.
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## Legal information
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The original model is distributed under the [apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. More details can be found in [training_extensions](https://github.com/open-edge-platform/geti).
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## Disclaimer
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Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel's Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel's products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
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config.json
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{
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"confidence_threshold": "0.05",
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"getitune_version": "0.1.0",
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"input_dtype": "u8",
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"intensity_min_value": "0.0",
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"intensity_mode": "scale_to_unit",
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"intensity_percentile_high": "99.0",
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"intensity_percentile_low": "1.0",
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"intensity_scale_factor": "1.0",
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"iou_threshold": "0.5",
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"label_ids": "None 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79",
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"label_info": "{\"label_names\": [\"getitune_empty_lbl\", \"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\", \"label_5\", \"label_6\", \"label_7\", \"label_8\", \"label_9\", \"label_10\", \"label_11\", \"label_12\", \"label_13\", \"label_14\", \"label_15\", \"label_16\", \"label_17\", \"label_18\", \"label_19\", \"label_20\", \"label_21\", \"label_22\", \"label_23\", \"label_24\", \"label_25\", \"label_26\", \"label_27\", \"label_28\", \"label_29\", \"label_30\", \"label_31\", \"label_32\", \"label_33\", \"label_34\", \"label_35\", \"label_36\", \"label_37\", \"label_38\", \"label_39\", \"label_40\", \"label_41\", \"label_42\", \"label_43\", \"label_44\", \"label_45\", \"label_46\", \"label_47\", \"label_48\", \"label_49\", \"label_50\", \"label_51\", \"label_52\", \"label_53\", \"label_54\", \"label_55\", \"label_56\", \"label_57\", \"label_58\", \"label_59\", \"label_60\", \"label_61\", \"label_62\", \"label_63\", \"label_64\", \"label_65\", \"label_66\", \"label_67\", \"label_68\", \"label_69\", \"label_70\", \"label_71\", \"label_72\", \"label_73\", \"label_74\", \"label_75\", \"label_76\", \"label_77\", \"label_78\", \"label_79\"], \"label_ids\": [\"None\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\", \"32\", \"33\", \"34\", \"35\", \"36\", \"37\", \"38\", \"39\", \"40\", \"41\", \"42\", \"43\", \"44\", \"45\", \"46\", \"47\", \"48\", \"49\", \"50\", \"51\", \"52\", \"53\", \"54\", \"55\", \"56\", \"57\", \"58\", \"59\", \"60\", \"61\", \"62\", \"63\", \"64\", \"65\", \"66\", \"67\", \"68\", \"69\", \"70\", \"71\", \"72\", \"73\", \"74\", \"75\", \"76\", \"77\", \"78\", \"79\"], \"label_groups\": [[\"getitune_empty_lbl\", \"label_0\", \"label_1\", \"label_2\", \"label_3\", \"label_4\", \"label_5\", \"label_6\", \"label_7\", \"label_8\", \"label_9\", \"label_10\", \"label_11\", \"label_12\", \"label_13\", \"label_14\", \"label_15\", \"label_16\", \"label_17\", \"label_18\", \"label_19\", \"label_20\", \"label_21\", \"label_22\", \"label_23\", \"label_24\", \"label_25\", \"label_26\", \"label_27\", \"label_28\", \"label_29\", \"label_30\", \"label_31\", \"label_32\", \"label_33\", \"label_34\", \"label_35\", \"label_36\", \"label_37\", \"label_38\", \"label_39\", \"label_40\", \"label_41\", \"label_42\", \"label_43\", \"label_44\", \"label_45\", \"label_46\", \"label_47\", \"label_48\", \"label_49\", \"label_50\", \"label_51\", \"label_52\", \"label_53\", \"label_54\", \"label_55\", \"label_56\", \"label_57\", \"label_58\", \"label_59\", \"label_60\", \"label_61\", \"label_62\", \"label_63\", \"label_64\", \"label_65\", \"label_66\", \"label_67\", \"label_68\", \"label_69\", \"label_70\", \"label_71\", \"label_72\", \"label_73\", \"label_74\", \"label_75\", \"label_76\", \"label_77\", \"label_78\", \"label_79\"]]}",
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"labels": "getitune_empty_lbl label_0 label_1 label_2 label_3 label_4 label_5 label_6 label_7 label_8 label_9 label_10 label_11 label_12 label_13 label_14 label_15 label_16 label_17 label_18 label_19 label_20 label_21 label_22 label_23 label_24 label_25 label_26 label_27 label_28 label_29 label_30 label_31 label_32 label_33 label_34 label_35 label_36 label_37 label_38 label_39 label_40 label_41 label_42 label_43 label_44 label_45 label_46 label_47 label_48 label_49 label_50 label_51 label_52 label_53 label_54 label_55 label_56 label_57 label_58 label_59 label_60 label_61 label_62 label_63 label_64 label_65 label_66 label_67 label_68 label_69 label_70 label_71 label_72 label_73 label_74 label_75 label_76 label_77 label_78 label_79",
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"mean_values": "0.406 0.456 0.485",
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"model_name": "rtmdet_inst_tiny",
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"model_type": "MaskRCNN",
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"nms_execute": "True",
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"optimization_config": "{}",
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"pad_value": "114",
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"resize_type": "fit_to_window_letterbox",
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"reverse_input_channels": "False",
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"scale_values": "0.225 0.224 0.229",
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"task_type": "instance_segmentation"
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}
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rtmdet_inst_tiny.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:ab16efd29dd8ef0b24bf730c1c926a7fdead0f190abbed721d4d97b73c59904e
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size 11922170
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rtmdet_inst_tiny.xml
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