HuggingFace镜像/inception_v3.tv_in1k
模型介绍文件和版本分析
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inception_v3.tv_in1k 模型卡片

一个 Inception-v3 图像分类模型。在 ImageNet-1k 上训练，使用 torchvision 权重。

模型详情

  • 模型类型： 图像分类 / 特征骨干网络
  • 模型统计：
    • 参数（百万）：23.8
    • 千兆次乘加运算（GMACs）：5.7
    • 激活值（百万）：9.0
    • 图像尺寸：299 x 299
  • 相关论文：
    • 《Rethinking the Inception Architecture for Computer Vision》（重新思考计算机视觉中的 Inception 架构）：https://arxiv.org/abs/1512.00567
  • 原始来源： https://github.com/pytorch/vision
  • 数据集： ImageNet-1k

模型使用

图像分类

from urllib.request import urlopen
from PIL import Image
import timm
import torch
import torch_npu

img = Image.open(urlopen(
    'beignets-task-guide.png'
))
device = torch.device('npu:0') if is_torch_npu_available() else torch.device('cpu')
model = timm.create_model('inception_v4.tf_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

特征图提取

from urllib.request import urlopen
from PIL import Image
import timm
import torch
import torch_npu

img = Image.open(urlopen(
    'beignets-task-guide.png'
))

model = timm.create_model(
    'inception_v3.tv_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 64, 147, 147])
    #  torch.Size([1, 192, 71, 71])
    #  torch.Size([1, 288, 35, 35])
    #  torch.Size([1, 768, 17, 17])
    #  torch.Size([1, 2048, 8, 8])

    print(o.shape)

图像嵌入

from urllib.request import urlopen
from PIL import Image
import timm
import torch
import torch_npu

img = Image.open(urlopen(
    'beignets-task-guide.png'
))

model = timm.create_model(
    'inception_v3.tv_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 2048, 8, 8) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

模型对比

在 timm 的 model results 中探索此模型的数据集和运行时指标。

引用

@article{DBLP:journals/corr/SzegedyVISW15,
  author    = {Christian Szegedy and
               Vincent Vanhoucke and
               Sergey Ioffe and
               Jonathon Shlens and
               Zbigniew Wojna},
  title     = {Rethinking the Inception Architecture for Computer Vision},
  journal   = {CoRR},
  volume    = {abs/1512.00567},
  year      = {2015},
  url       = {http://arxiv.org/abs/1512.00567},
  archivePrefix = {arXiv},
  eprint    = {1512.00567},
  timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}