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onnxmodelzoo/tf_efficientnetv2_l_in21ft1k_Opset17

By onnxmodelzoo · ↓ 0 · ♥ 0 · Updated 2025-09-25

What's in this repo

This page lists the files in onnxmodelzoo/tf_efficientnetv2_l_in21ft1k_Opset17, including tf_efficientnetv2_l_in21ft1k_Opset17.onnx.

  • 1 ONNX models (.onnx files)
  • 4 files, including 1 .md, 1 .onnx, 1 .yaml
  • Last updated 2025-09-25

ONNX files

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tf_efficientnetv2_l_in21ft1k_Opset17.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README


language: en license: apache-2.0 model_name: tf_efficientnetv2_l_in21ft1k_Opset17.onnx tags:

  • Computer_Vision

The README content appears to be empty. Let me fetch it from the HuggingFace model page.

language: en license: apache-2.0 model_name: tf_efficientnetv2_l_in21ft1k_Opset17.onnx tags:

  • 计算机视觉

注意:此仓库包含 ONNX 格式的模型。

模型来源: 该模型从 timm 转换而来。

模型描述: 该模型是从 EfficientNetV2 Large 模型转换而来,在 ImageNet-21K 数据集上进行了预训练,并在 ImageNet-1K 上进行了微调。

使用方法: 您可以使用 ONNX Runtime 来加载和运行该模型。以下是一个简单的 Python 示例:

import onnxruntime as ort
import numpy as np
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

# 加载模型
session = ort.InferenceSession("tf_efficientnetv2_l_in21ft1k_Opset17.onnx")

# 准备输入数据
config = resolve_data_config({}, model=session)
transform = create_transform(**config)

img = Image.open("your_image.jpg").convert("RGB")
input_tensor = transform(img).unsqueeze(0).numpy()

# 运行推理
outputs = session.run(None, {"input": input_tensor})

# 获取预测结果
predicted_class = np.argmax(outputs[0])
print(f"预测类别: {predicted_class}")

相关信息:

  • Opset 版本: 17
  • 原始模型: tf_efficientnetv2_l_in21ft1k

Model info

onnx Computer_Vision en license:apache-2.0 region:us
↓ 0 ♥ 0

Created: 2025-09-25

Updated: 2025-09-25

View on Hugging Face

Files (4)

.gitattributes
README.md
tf_efficientnetv2_l_in21ft1k_Opset17.onnx ONNX
turnkey_stats.yaml

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