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Model creator: timm Original model: timm/mobilenetv2120d.rain1k
Fuente del modelo
Descripción de la fuente
This is model converted to the (Intermediate Representation) format with weights compressed to INT8 by .
Fuentes
1 fuenteVerificado 27 ago
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractosWeight 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/mobilenetv2_120d_ra_in1k-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 card for limitations.
The original model is distributed under the Apache-2.0 license. More details can be found in timm/mobilenetv2_120d.ra_in1k
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: - image-classification - vision base_model: - timm/mobilenetv2_120d.ra_in1k base_model_relation: quantized --- # mobilenetv2_120d_ra_in1k-int8-ov - Model creator: [timm](https://huggingface.co/timm) - Original model: [timm/mobilenetv2_120d.ra_in1k](https://huggingface.co/timm/mobilenetv2_120d.ra_in1k) ## Description This is [timm/mobilenetv2_120d.ra_in1k](https://huggingface.co/timm/mobilenetv2_120d.ra_in1k) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf). ## 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/mobilenetv2_120d_ra_in1k-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 card](https://huggingface.co/timm/mobilenetv2_120d.ra_in1k) for limitations. ## Legal information The original model is distributed under the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. More details can be found in [timm/mobilenetv2_120d.ra_in1k](https://huggingface.co/timm/mobilenetv2_120d.ra_in1k) ## Discl...
Source context: 15 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/mobilenetv2_120d_ra_in1k-int8-ov