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README.md

@@ -0,0 +1,79 @@
+---
+license: apache-2.0
+tags:
+  - image-segmentation
+  - instance-segmentation
+  - vision
+---
+
+# maskrcnn_swint-int8-ov
+
+- Model creator: [Geti™](https://github.com/open-edge-platform/geti)
+- Original model: [Mask R-CNN Swin-Tiny](https://github.com/open-mmlab/mmdetection/tree/main/configs/swin)
+
+## Description
+
+This is a [Geti™](https://github.com/open-edge-platform/geti) version of [Mask R-CNN Swin-Tiny](https://github.com/open-mmlab/mmdetection/tree/main/configs/swin) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8.
+
+To fine-tune your model with a custom dataset, you can use Geti™ to annotate data, perform fine-tuning, and export the resulting model.
+
+## Quantization Parameters
+
+Weight compression was performed using nncf.quantize with the following parameters:
+
+- **Quantization method**: Post-Training Quantization (PTQ)
+- **Precision**: INT8 for both weights and activations
+  
+  For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2026/openvino-workflow/model-optimization-guide/quantizing-models-post-training.html).
+
+## Compatibility
+
+The provided OpenVINO™ IR model is compatible with:
+
+- OpenVINO version 2026.1.0 and higher
+- Model API 0.4.0 and higher
+
+## Running Model Inference with [Model API](https://github.com/open-edge-platform/model_api)
+
+1. Install required packages:
+
+```sh
+pip install openvino-model-api[huggingface]
+```
+
+<!-- markdownlint-disable MD029 -->
+
+2. Run model inference:
+
+```python
+import cv2
+from model_api.models import Model
+from model_api.visualizer import Visualizer
+
+# 1. Load model
+model = Model.from_pretrained("OpenVINO/maskrcnn_swint-int8-ov")
+
+# 2. Load image
+image = cv2.imread("image.jpg")
+
+# 3. Run inference
+result = model(image)
+
+# 4. Visualize and save results
+vis = Visualizer().render(image, result)
+cv2.imwrite("output.jpg", vis)
+```
+
+For more examples and possible optimizations, refer to the [Model API Documentation](https://open-edge-platform.github.io/model_api/latest/).
+
+## Limitations
+
+Check the [original model documentation](https://github.com/open-mmlab/mmdetection/tree/main/configs/swin) for limitations.
+
+## Legal information
+
+The original model is distributed under the [Apache-2.0](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE) license. More details can be found in the [original model repository](https://github.com/open-mmlab/mmdetection/tree/main/configs/swin).
+
+## Disclaimer
+
+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.

config.json

@@ -0,0 +1,24 @@
+{
+  "confidence_threshold": "0.05",
+  "getitune_version": "0.1.0",
+  "input_dtype": "u8",
+  "intensity_min_value": "0.0",
+  "intensity_mode": "scale_to_unit",
+  "intensity_percentile_high": "99.0",
+  "intensity_percentile_low": "1.0",
+  "intensity_scale_factor": "1.0",
+  "iou_threshold": "0.5",
+  "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_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\"]]}",
+  "labels": "__background__ person bicycle car motorcycle airplane bus train truck boat traffic_light fire_hydrant stop_sign parking_meter bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard sports_ball kite baseball_bat baseball_glove skateboard surfboard tennis_racket bottle wine_glass cup fork knife spoon bowl banana apple sandwich orange broccoli carrot hot_dog pizza donut cake chair couch potted_plant bed dining_table toilet tv laptop mouse remote keyboard cell_phone microwave oven toaster sink refrigerator book clock vase scissors teddy_bear hair_drier toothbrush",
+  "mean_values": "0.485 0.456 0.406",
+  "model_name": "maskrcnn_swin_tiny",
+  "model_type": "MaskRCNN",
+  "nms_execute": "True",
+  "optimization_config": "{}",
+  "pad_value": "0",
+  "resize_type": "fit_to_window",
+  "reverse_input_channels": "False",
+  "scale_values": "0.229 0.224 0.225",
+  "task_type": "instance_segmentation"
+}

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