EfficientNet-Lite4 is an image classification model that achieves state-of-the-art accuracy. It is designed to run on mobile CPU, GPU, and EdgeTPU devices, allowing for applications on mobile and loT, where computational resources are limited.
EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU.
| Model | Download | Download (with sample test data) | ONNX version | Opset version | Top-1 accuracy (%) |
|---|---|---|---|---|---|
| EfficientNet-Lite4 | 51.9 MB | 48.6 MB | 1.7.0 | 11 | 80.4 |
| EfficientNet-Lite4-int8 | 13.0 MB | 12.2 MB | 1.9.0 | 11 | 77.56 |
The fp32 Top-1 accuracy got by Intel® Neural Compressor is 77.70%, and compared with this value, int8 EfficientNet-Lite4's Top-1 accuracy drop ratio is 0.18% and performance improvement is 1.12x.
Note
The performance depends on the test hardware. Performance data here is collected with Intel® Xeon® Platinum 8280 Processor, 1s 4c per instance, CentOS Linux 8.3, data batch size is 1.
Tensorflow EfficientNet-Lite4 => ONNX EfficientNet-Lite4 ONNX EfficientNet-Lite4 => Quantized ONNX EfficientNet-Lite4
The following steps show how to run the inference using onnxruntime.
import onnxruntime as rt
# load model
# Start from ORT 1.10, ORT requires explicitly setting the providers parameter if you want to use execution providers
# other than the default CPU provider (as opposed to the previous behavior of providers getting set/registered by default
# based on the build flags) when instantiating InferenceSession.
# For example, if NVIDIA GPU is available and ORT Python package is built with CUDA, then call API as following:
# rt.InferenceSession(path/to/model, providers=['CUDAExecutionProvider'])
sess = rt.InferenceSession(MODEL + ".onnx")
# run inference
results = sess.run(["Softmax:0"], {"images:0": img_batch})[0]
Input image to model is resized to shape float32[1,224,224,3]. The batch size is 1, with 224 x 224 height and width dimensions. The input is an RBG image that has 3 channels: red, green, and blue. Inference was done using a jpg image.
The following steps show how to preprocess the input image. For more details visit this conversion notebook.
import numpy as np
import math
import matplotlib.pyplot as plt
import onnxruntime as rt
import cv2
import json
# load the labels text file
labels = json.load(open("labels_map.txt", "r"))
# set image file dimensions to 224x224 by resizing and cropping image from center
def pre_process_edgetpu(img, dims):
output_height, output_width, _ = dims
img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)
img = center_crop(img, output_height, output_width)
img = np.asarray(img, dtype='float32')
# converts jpg pixel value from [0 - 255] to float array [-1.0 - 1.0]
img -= [127.0, 127.0, 127.0]
img /= [128.0, 128.0, 128.0]
return img
# resize the image with a proportional scale
def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
height, width, _ = img.shape
new_height = int(100. * out_height / scale)
new_width = int(100. * out_width / scale)
if height > width:
w = new_width
h = int(new_height * height / width)
else:
h = new_height
w = int(new_width * width / height)
img = cv2.resize(img, (w, h), interpolation=inter_pol)
return img
# crop the image around the center based on given height and width
def center_crop(img, out_height, out_width):
height, width, _ = img.shape
left = int((width - out_width) / 2)
right = int((width + out_width) / 2)
top = int((height - out_height) / 2)
bottom = int((height + out_height) / 2)
img = img[top:bottom, left:right]
return img
# read the image
fname = "image_file"
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pre-process the image like mobilenet and resize it to 224x224
img = pre_process_edgetpu(img, (224, 224, 3))
plt.axis('off')
plt.imshow(img)
plt.show()
# create a batch of 1 (that batch size is buned into the saved_model)
img_batch = np.expand_dims(img, axis=0)
Output of model is an inference score with array shape float32[1,1000]. The output references the labels_map.txt file which maps an index to a label to classify the type of image.
The following steps detail how to print the output results of the model.
# load the model
# Start from ORT 1.10, ORT requires explicitly setting the providers parameter if you want to use execution providers
# other than the default CPU provider (as opposed to the previous behavior of providers getting set/registered by default
# based on the build flags) when instantiating InferenceSession.
# For example, if NVIDIA GPU is available and ORT Python package is built with CUDA, then call API as following:
# rt.InferenceSession(path/to/model, providers=['CUDAExecutionProvider'])
sess = rt.InferenceSession(MODEL + ".onnx")
# run inference and print results
results = sess.run(["Softmax:0"], {"images:0": img_batch})[0]
result = reversed(results[0].argsort()[-5:])
for r in result:
print(r, labels[str(r)], results[0][r])
The model was trained using COCO 2017 Train Images, Val Images, and Train/Val annotations.
Refer to efficientnet-lite4 conversion notebook for details of how to use it and reproduce accuracy.
CaffeNet-int8 is obtained by quantizing fp32 CaffeNet model. We use Intel® Neural Compressor with onnxruntime backend to perform quantization. View the instructions to understand how to use Intel® Neural Compressor for quantization.
onnx: 1.9.0 onnxruntime: 1.8.0
wget https://github.com/onnx/models/raw/main/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnxMake sure to specify the appropriate dataset path in the configuration file.
bash run_tuning.sh --input_model=path/to/model \ # model path as *.onnx
--config=efficientnet.yaml \
--output_model=path/to/save-
Tensorflow to Onnx conversion tutorial. The Juypter Notebook references how to run an evaluation on the efficientnet-lite4 model and export it as a saved model. It also details how to convert the tensorflow model into onnx, and how to run its preprocessing and postprocessing code for the inputs and outputs.
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Refer to this paper for more details on the model.
- Shirley Su
- mengniwang95 (Intel)
- airMeng (Intel)
- ftian1 (Intel)
- hshen14 (Intel)
MIT License