File size: 2,910 Bytes
c51a3d0 2b987ee c51a3d0 2b987ee c51a3d0 2b987ee c51a3d0 2b987ee c51a3d0 | 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 | ---
license: apache-2.0
tags:
- object-detection
- vision
---
# yolox_s-int8-ov
- Model creator: [Geti™](https://github.com/open-edge-platform/geti)
- Original model: [YOLOX-S](https://github.com/open-mmlab/mmdetection/tree/main/configs/yolox)
## Description
This is a [Geti™](https://github.com/open-edge-platform/geti) version of [YOLOX-S](https://github.com/open-mmlab/mmdetection/tree/main/configs/yolox) 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/yolox_s-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/yolox) 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/yolox).
## 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.
|