Yolox Tiny FP16 Ov

--- license: apache-2.0 tags: - object-detection - vision --- # yolox_tiny-fp16-ov - Model creator: [Geti™](https://github.com/open-edge-platform/geti) - Original model: [YOLOX-Tiny](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-Tiny](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 FP16. To fine-tune your model with a custom dataset, you can use Geti™ to annotate data, perform fine-tuning, and export the resulting model. ## 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_tiny-fp16-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 us...

Source context: 133 downloads · 1 likes · Pipeline object-detection · Repo OpenVINO/yolox_tiny-fp16-ov

Source context: 27 downloads · 0 likes · Pipeline object-detection · Repo OpenVINO/yolox_tiny-fp16-ov