timm / tf_efficientnet_b0.ns_jft_in1k

huggingface.co
Total runs: 59.0K
24-hour runs: 0
7-day runs: 5.4K
30-day runs: 25.8K
Model's Last Updated: 1월 21 2025
image-classification

Introduction of tf_efficientnet_b0.ns_jft_in1k

Model Details of tf_efficientnet_b0.ns_jft_in1k

Model card for tf_efficientnet_b0.ns_jft_in1k

A EfficientNet image classification model. Trained on ImageNet-1k and unlabeled JFT-300m using Noisy Student semi-supervised learning in Tensorflow by paper authors, ported to PyTorch by Ross Wightman.

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('tf_efficientnet_b0.ns_jft_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(
    'tf_efficientnet_b0.ns_jft_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, 16, 112, 112])
    #  torch.Size([1, 24, 56, 56])
    #  torch.Size([1, 40, 28, 28])
    #  torch.Size([1, 112, 14, 14])
    #  torch.Size([1, 320, 7, 7])

    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(
    'tf_efficientnet_b0.ns_jft_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, 1280, 7, 7) 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
@inproceedings{tan2019efficientnet,
  title={Efficientnet: Rethinking model scaling for convolutional neural networks},
  author={Tan, Mingxing and Le, Quoc},
  booktitle={International conference on machine learning},
  pages={6105--6114},
  year={2019},
  organization={PMLR}
}
@article{Xie2019SelfTrainingWN,
  title={Self-Training With Noisy Student Improves ImageNet Classification},
  author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={10684-10695}
}
@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}

Runs of timm tf_efficientnet_b0.ns_jft_in1k on huggingface.co

59.0K
Total runs
0
24-hour runs
2.1K
3-day runs
5.4K
7-day runs
25.8K
30-day runs

More Information About tf_efficientnet_b0.ns_jft_in1k huggingface.co Model

More tf_efficientnet_b0.ns_jft_in1k license Visit here:

https://choosealicense.com/licenses/apache-2.0

tf_efficientnet_b0.ns_jft_in1k huggingface.co

tf_efficientnet_b0.ns_jft_in1k huggingface.co is an AI model on huggingface.co that provides tf_efficientnet_b0.ns_jft_in1k's model effect (), which can be used instantly with this timm tf_efficientnet_b0.ns_jft_in1k model. huggingface.co supports a free trial of the tf_efficientnet_b0.ns_jft_in1k model, and also provides paid use of the tf_efficientnet_b0.ns_jft_in1k. Support call tf_efficientnet_b0.ns_jft_in1k model through api, including Node.js, Python, http.

tf_efficientnet_b0.ns_jft_in1k huggingface.co Url

https://huggingface.co/timm/tf_efficientnet_b0.ns_jft_in1k

timm tf_efficientnet_b0.ns_jft_in1k online free

tf_efficientnet_b0.ns_jft_in1k huggingface.co is an online trial and call api platform, which integrates tf_efficientnet_b0.ns_jft_in1k's modeling effects, including api services, and provides a free online trial of tf_efficientnet_b0.ns_jft_in1k, you can try tf_efficientnet_b0.ns_jft_in1k online for free by clicking the link below.

timm tf_efficientnet_b0.ns_jft_in1k online free url in huggingface.co:

https://huggingface.co/timm/tf_efficientnet_b0.ns_jft_in1k

tf_efficientnet_b0.ns_jft_in1k install

tf_efficientnet_b0.ns_jft_in1k is an open source model from GitHub that offers a free installation service, and any user can find tf_efficientnet_b0.ns_jft_in1k on GitHub to install. At the same time, huggingface.co provides the effect of tf_efficientnet_b0.ns_jft_in1k install, users can directly use tf_efficientnet_b0.ns_jft_in1k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

tf_efficientnet_b0.ns_jft_in1k install url in huggingface.co:

https://huggingface.co/timm/tf_efficientnet_b0.ns_jft_in1k

Url of tf_efficientnet_b0.ns_jft_in1k

tf_efficientnet_b0.ns_jft_in1k huggingface.co Url

Provider of tf_efficientnet_b0.ns_jft_in1k huggingface.co

timm
ORGANIZATIONS

Other API from timm

huggingface.co

Total runs: 19.0M
Run Growth: -1.8M
Growth Rate: -9.67%
Updated: 1월 21 2025
huggingface.co

Total runs: 125.5K
Run Growth: 103.6K
Growth Rate: 82.60%
Updated: 10월 25 2023
huggingface.co

Total runs: 113.9K
Run Growth: -112.3K
Growth Rate: -100.13%
Updated: 1월 21 2025
huggingface.co

Total runs: 31.4K
Run Growth: 8.7K
Growth Rate: 27.99%
Updated: 1월 21 2025
huggingface.co

Total runs: 24.0K
Run Growth: 7.5K
Growth Rate: 31.41%
Updated: 1월 21 2025
huggingface.co

Total runs: 20.8K
Run Growth: 6.5K
Growth Rate: 32.11%
Updated: 1월 21 2025
huggingface.co

Total runs: 20.5K
Run Growth: 6.0K
Growth Rate: 30.02%
Updated: 1월 21 2025
huggingface.co

Total runs: 20.5K
Run Growth: 5.8K
Growth Rate: 28.79%
Updated: 1월 21 2025
huggingface.co

Total runs: 20.2K
Run Growth: 15.2K
Growth Rate: 76.29%
Updated: 1월 21 2025