This is a
Geti™ version of
YOLOX-L model converted to the
OpenVINO™ IR (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.
Weight compression was performed using nncf.quantize with the following parameters:
For more information on quantization, check the
OpenVINO model optimization guide.
1import cv2
2from model_api.models import Model
3from model_api.visualizer import Visualizer
4
5# 1. Load model
6model = Model.from_pretrained("OpenVINO/yolox_l-int8-ov")
7
8# 2. Load image
9image = cv2.imread("image.jpg")
10
11# 3. Run inference
12result = model(image)
13
14# 4. Visualize and save results
15vis = Visualizer().render(image, result)
16cv2.imwrite("output.jpg", vis)
For more examples and possible optimizations, refer to the
Model API Documentation.
The original model is distributed under the
Apache-2.0 license. More details can be found in the
original model repository.
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Intel's Global Human Rights Principles. 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.
