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 |
|---|---|---|---|---|
| EfficientNet-Lite4 | 51.9 MB | 48.6 MB | 1.7.0 | 11 |
Tensorflow EfficientNet-Lite4 => ONNX EfficientNet-Lite4
The following steps show how to run the inference using onnxruntime.
import onnxruntime as rt
# load model
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
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.
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.
Refer to this paper for more details on the model.
MIT License