模型中心
WeightNet
WeightNet
vision
classification
WeightNet - ShuffleNet V2(ImageNet 预训练权重)
import megengine.hub
model = megengine.hub.load('megvii-model/weightnet', 'shufflenet_v2_x0_5', pretrained=True)
model.eval()
所有预训练模型希望数据被正确预处理。
模型要求输入BGR的图片, 短边缩放到256
, 并中心裁剪至(224 x 224)
的大小,无需归一化处理。
下面是一段处理一张图片的样例代码。
# Download an example image from the megengine data website
import urllib
url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)
# Read and pre-process the image
import cv2
import numpy as np
import megengine.data.transform as T
import megengine.functional as F
image = cv2.imread("cat.jpg").astype(np.float32)
transform = T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.ToMode("CHW"),
])
processed_img = transform.apply(image)[np.newaxis, :] # CHW -> 1CHW
logits = model(processed_img)
probs = F.softmax(logits)
print(probs)
模型描述
目前我们提供了部分在ImageNet上的预训练模型(见下表),各个网络结构在ImageNet验证集上的表现如下:
Model | #Params. | FLOPs | Top-1 err. |
---|---|---|---|
ShuffleNetV2 (0.5×) | 1.4M | 41M | 39.7 |
+ WeightNet (1×) | 1.5M | 41M | 36.7 |
ShuffleNetV2 (1.0×) | 2.2M | 138M | 30.9 |
+ WeightNet (1×) | 2.4M | 139M | 28.8 |
ShuffleNetV2 (1.5×) | 3.5M | 299M | 27.4 |
+ WeightNet (1×) | 3.9M | 301M | 25.6 |
ShuffleNetV2 (2.0×) | 5.5M | 557M | 25.5 |
+ WeightNet (1×) | 6.1M | 562M | 24.1 |
Model | #Params. | FLOPs | Top-1 err. |
---|---|---|---|
ShuffleNetV2 (0.5×) | 1.4M | 41M | 39.7 |
+ WeightNet (8×) | 2.7M | 42M | 34.0 |
ShuffleNetV2 (1.0×) | 2.2M | 138M | 30.9 |
+ WeightNet (4×) | 5.1M | 141M | 27.6 |
ShuffleNetV2 (1.5×) | 3.5M | 299M | 27.4 |
+ WeightNet (4×) | 9.6M | 307M | 25.0 |
ShuffleNetV2 (2.0×) | 5.5M | 557M | 25.5 |
+ WeightNet (4×) | 18.1M | 573M | 23.5 |
参考文献
- WeightNet: Revisiting the Design Space of Weight Network, Ma, Ningning, et al. "WeightNet: Revisiting the Design Space of Weight Network." Proceedings of the European Conference on Computer Vision (ECCV). 2020.