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Model creator: GetiTM Original model: DEIMv2-M
Source du modèle
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1 sourceVérifié 18 août
Artefacts du modèle
1 artefactExtraits de sources
2 extraitsTo fine-tune your model with a custom dataset, you can use GetiTM 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.
The provided OpenVINOTM IR model is compatible with:
pip install openvino-model-api[huggingface]
import cv2
from model_api.models import Model
from model_api.visualizer import Visualizer
# 1. Load model
model = Model.from_pretrained("OpenVINO/deimv2_m-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.
Check the original model documentation for limitations.
The original model is distributed under the Apache-2.0 license. More details can be found in the original model repository.
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. 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.
--- license: apache-2.0 tags: - object-detection - vision --- # deimv2_m-int8-ov - Model creator: [Geti™](https://github.com/open-edge-platform/geti) - Original model: [DEIMv2-M](https://github.com/Intellindust-AI-Lab/DEIMv2) ## Description This is a [Geti™](https://github.com/open-edge-platform/geti) version of [DEIMv2-M](https://github.com/Intellindust-AI-Lab/DEIMv2) 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/deimv2_m-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/Intellindust-AI-Lab/DEIMv2) for limitations. ## Legal information The original model is distributed under the [Apache-2.0](https://github.com/Intellindust-AI-Lab/DEIM/blob/main/LICENSE) license. More details can be found in the [original model repository](https://github.com/Intellindust-AI-Lab/DEI...
Source context: 12 downloads · 0 likes · Pipeline object-detection · Repo OpenVINO/deimv2_m-int8-ov