timm / pnasnet5large.tf_in1k

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image-classification

Introduction of pnasnet5large.tf_in1k

Model Details of pnasnet5large.tf_in1k

Model card for pnasnet5large.tf_in1k

A PNasNet image classification model. Trained on ImageNet-1k by paper authors. Ported from Tensorflow via Cadene's pretrained-models.pytorch.

Model Details
Model Usage
Image Classification
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('pnasnet5large.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)
Feature Map Extraction
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'pnasnet5large.tf_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, 96, 165, 165])
    #  torch.Size([1, 270, 83, 83])
    #  torch.Size([1, 1080, 42, 42])
    #  torch.Size([1, 2160, 21, 21])
    #  torch.Size([1, 4320, 11, 11])

    print(o.shape)
Image Embeddings
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'pnasnet5large.tf_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, 4320, 11, 11) shaped tensor

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

Explore the dataset and runtime metrics of this model in timm model results .

Citation
@misc{liu2018progressive,
  title={Progressive Neural Architecture Search}, 
  author={Chenxi Liu and Barret Zoph and Maxim Neumann and Jonathon Shlens and Wei Hua and Li-Jia Li and Li Fei-Fei and Alan Yuille and Jonathan Huang and Kevin Murphy},
  year={2018},
  eprint={1712.00559},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Runs of timm pnasnet5large.tf_in1k on huggingface.co

20.8K
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pnasnet5large.tf_in1k install

pnasnet5large.tf_in1k is an open source model from GitHub that offers a free installation service, and any user can find pnasnet5large.tf_in1k on GitHub to install. At the same time, huggingface.co provides the effect of pnasnet5large.tf_in1k install, users can directly use pnasnet5large.tf_in1k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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