timm

timm/regnety_016.tv2_in1k

Model Type: Image classification / feature backbone - Model Stats: - Params (M): 11.2 - GMACs: 1.6 - Activations (M): 8.0 - Image size: 224 ...

Model Documentation

Model card for regnety_016.tv2_in1k

A RegNetY-1.6GF image classification model. Pretrained on ImageNet-1k by torchvision contributors (see ImageNet1K-V2 weight details https://github.com/pytorch/vision/issues/3995#new-recipe).

The timm RegNet implementation includes a number of enhancements not present in other implementations, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * configurable output stride (dilation) * configurable activation and norm layers * option for a pre-activation bottleneck block used in RegNetV variant * only known RegNetZ model definitions with pretrained weights

Model Details

Model Type: Image classification / feature backbone

Model Stats:

Params (M): 11.2

GMACs: 1.6

Activations (M): 8.0

Image size: 224 x 224

Papers:

Designing Network Design Spaces: https://arxiv.org/abs/2003.13678

Original: https://github.com/pytorch/vision

Model Usage

Image Classification

python
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('regnety_016.tv2_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

python
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(
    'regnety_016.tv2_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, 32, 112, 112])
     torch.Size([1, 48, 56, 56])
     torch.Size([1, 120, 28, 28])
     torch.Size([1, 336, 14, 14])
     torch.Size([1, 888, 7, 7])


    print(o.shape)

Image Embeddings

python
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(
    'regnety_016.tv2_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, 888, 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.

For the comparison summary below, the ra_in1k, ra3_in1k, ch_in1k, sw_*, and lion_* tagged weights are trained in timm. |model |img_size|top1 |top5 |param_count|gmacs|macts | |-------------------------|--------|------|------|-----------|-----|------| |regnety_1280.swag_ft_in1k|384 |88.228|98.684|644.81 |374.99|210.2 | |regnety_320.swag_ft_in1k|384 |86.84 |98.364|145.05 |95.0 |88.87 | |regnety_160.swag_ft_in1k|384 |86.024|98.05 |83.59 |46.87|67.67 | |regnety_160.sw_in12k_ft_in1k|288 |86.004|97.83 |83.59 |26.37|38.07 | |regnety_1280.swag_lc_in1k|224 |85.996|97.848|644.81 |127.66|71.58 | |regnety_160.lion_in12k_ft_in1k|288 |85.982|97.844|83.59 |26.37|38.07 | |regnety_160.sw_in12k_ft_in1k|224 |85.574|97.666|83.59 |15.96|23.04 | |regnety_160.lion_in12k_ft_in1k|224 |85.564|97.674|83.59 |15.96|23.04 | |regnety_120.sw_in12k_ft_in1k|288 |85.398|97.584|51.82 |20.06|35.34 | |regnety_2560.seer_ft_in1k|384 |85.15 |97.436|1282.6 |747.83|296.49| |regnetz_e8.ra3_in1k|320 |85.036|97.268|57.7 |15.46|63.94 | |regnety_120.sw_in12k_ft_in1k|224 |84.976|97.416|51.82 |12.14|21.38 | |regnety_320.swag_lc_in1k|224 |84.56 |97.446|145.05 |32.34|30.26 | |regnetz_040_h.ra3_in1k|320 |84.496|97.004|28.94 |6.43 |37.94 | |regnetz_e8.ra3_in1k|256 |84.436|97.02 |57.7 |9.91 |40.94 | |regnety_1280.seer_ft_in1k|384 |84.432|97.092|644.81 |374.99|210.2 | |regnetz_040.ra3_in1k|320 |84.246|96.93 |27.12 |6.35 |37.78 | |regnetz_d8.ra3_in1k|320 |84.054|96.992|23.37 |6.19 |37.08 | |regnetz_d8_evos.ch_in1k|320 |84.038|96.992|23.46 |7.03 |38.92 | |regnetz_d32.ra3_in1k|320 |84.022|96.866|27.58 |9.33 |37.08 | |regnety_080.ra3_in1k|288 |83.932|96.888|39.18 |13.22|29.69 | |regnety_640.seer_ft_in1k|384 |83.912|96.924|281.38 |188.47|124.83| |regnety_160.swag_lc_in1k|224 |83.778|97.286|83.59 |15.96|23.04 | |regnetz_040_h.ra3_in1k|256 |83.776|96.704|28.94 |4.12 |24.29 | |regnetv_064.ra3_in1k|288 |83.72 |96.75 |30.58 |10.55|27.11 | |regnety_064.ra3_in1k|288 |83.718|96.724|30.58 |10.56|27.11 | |regnety_160.deit_in1k|288 |83.69 |96.778|83.59 |26.37|38.07 | |regnetz_040.ra3_in1k|256 |83.62 |96.704|27.12 |4.06 |24.19 | |regnetz_d8.ra3_in1k|256 |83.438|96.776|23.37 |3.97 |23.74 | |regnetz_d32.ra3_in1k|256 |83.424|96.632|27.58 |5.98 |23.74 | |regnetz_d8_evos.ch_in1k|256 |83.36 |96.636|23.46 |4.5 |24.92 | |regnety_320.seer_ft_in1k|384 |83.35 |96.71 |145.05 |95.0 |88.87 | |regnetv_040.ra3_in1k|288 |83.204|96.66 |20.64 |6.6 |20.3 | |regnety_320.tv2_in1k|224 |83.162|96.42 |145.05 |32.34|30.26 | |regnety_080.ra3_in1k|224 |83.16 |96.486|39.18 |8.0 |17.97 | |regnetv_064.ra3_in1k|224 |83.108|96.458|30.58 |6.39 |16.41 | |regnety_040.ra3_in1k|288 |83.044|96.5 |20.65 |6.61 |20.3 | |regnety_064.ra3_in1k|224 |83.02 |96.292|30.58 |6.39 |16.41 | |regnety_160.deit_in1k|224 |82.974|96.502|83.59 |15.96|23.04 | |regnetx_320.tv2_in1k|224 |82.816|96.208|107.81 |31.81|36.3 | |regnety_032.ra_in1k|288 |82.742|96.418|19.44 |5.29 |18.61 | |regnety_160.tv2_in1k|224 |82.634|96.22 |83.59 |15.96|23.04 | |regnetz_c16_evos.ch_in1k|320 |82.634|96.472|13.49 |3.86 |25.88 | |regnety_080_tv.tv2_in1k|224 |82.592|96.246|39.38 |8.51 |19.73 | |regnetx_160.tv2_in1k|224 |82.564|96.052|54.28 |15.99|25.52 | |regnetz_c16.ra3_in1k|320 |82.51 |96.358|13.46 |3.92 |25.88 | |regnetv_040.ra3_in1k|224 |82.44 |96.198|20.64 |4.0 |12.29 | |regnety_040.ra3_in1k|224 |82.304|96.078|20.65 |4.0 |12.29 | |regnetz_c16.ra3_in1k|256 |82.16 |96.048|13.46 |2.51 |16.57 | |regnetz_c16_evos.ch_in1k|256 |81.936|96.15 |13.49 |2.48 |16.57 | |regnety_032.ra_in1k|224 |81.924|95.988|19.44 |3.2 |11.26 | |regnety_032.tv2_in1k|224 |81.77 |95.842|19.44 |3.2 |11.26 | |regnetx_080.tv2_in1k|224 |81.552|95.544|39.57 |8.02 |14.06 | |regnetx_032.tv2_in1k|224 |80.924|95.27 |15.3 |3.2 |11.37 | |regnety_320.pycls_in1k|224 |80.804|95.246|145.05 |32.34|30.26 | |regnetz_b16.ra3_in1k|288 |80.712|95.47 |9.72 |2.39 |16.43 | |regnety_016.tv2_in1k|224 |80.66 |95.334|11.2 |1.63 |8.04 | |regnety_120.pycls_in1k|224 |80.37 |95.12 |51.82 |12.14|21.38 | |regnety_160.pycls_in1k|224 |80.288|94.964|83.59 |15.96|23.04 | |regnetx_320.pycls_in1k|224 |80.246|95.01 |107.81 |31.81|36.3 | |regnety_080.pycls_in1k|224 |79.882|94.834|39.18 |8.0 |17.97 | |regnetz_b16.ra3_in1k|224 |79.872|94.974|9.72 |1.45 |9.95 | |regnetx_160.pycls_in1k|224 |79.862|94.828|54.28 |15.99|25.52 | |regnety_064.pycls_in1k|224 |79.716|94.772|30.58 |6.39 |16.41 | |regnetx_120.pycls_in1k|224 |79.592|94.738|46.11 |12.13|21.37 | |regnetx_016.tv2_in1k|224 |79.44 |94.772|9.19 |1.62 |7.93 | |regnety_040.pycls_in1k|224 |79.23 |94.654|20.65 |4.0 |12.29 | |regnetx_080.pycls_in1k|224 |79.198|94.55 |39.57 |8.02 |14.06 | |regnetx_064.pycls_in1k|224 |79.064|94.454|26.21 |6.49 |16.37 | |regnety_032.pycls_in1k|224 |78.884|94.412|19.44 |3.2 |11.26 | |regnety_008_tv.tv2_in1k|224 |78.654|94.388|6.43 |0.84 |5.42 | |regnetx_040.pycls_in1k|224 |78.482|94.24 |22.12 |3.99 |12.2 | |regnetx_032.pycls_in1k|224 |78.178|94.08 |15.3 |3.2 |11.37 | |regnety_016.pycls_in1k|224 |77.862|93.73 |11.2 |1.63 |8.04 | |regnetx_008.tv2_in1k|224 |77.302|93.672|7.26 |0.81 |5.15 | |regnetx_016.pycls_in1k|224 |76.908|93.418|9.19 |1.62 |7.93 | |regnety_008.pycls_in1k|224 |76.296|93.05 |6.26 |0.81 |5.25 | |regnety_004.tv2_in1k|224 |75.592|92.712|4.34 |0.41 |3.89 | |regnety_006.pycls_in1k|224 |75.244|92.518|6.06 |0.61 |4.33 | |regnetx_008.pycls_in1k|224 |75.042|92.342|7.26 |0.81 |5.15 | |regnetx_004_tv.tv2_in1k|224 |74.57 |92.184|5.5 |0.42 |3.17 | |regnety_004.pycls_in1k|224 |74.018|91.764|4.34 |0.41 |3.89 | |regnetx_006.pycls_in1k|224 |73.862|91.67 |6.2 |0.61 |3.98 | |regnetx_004.pycls_in1k|224 |72.38 |90.832|5.16 |0.4 |3.14 | |regnety_002.pycls_in1k|224 |70.282|89.534|3.16 |0.2 |2.17 | |regnetx_002.pycls_in1k|224 |68.752|88.556|2.68 |0.2 |2.16 |

Citation

bibtex
@InProceedings{Radosavovic2020,
  title = {Designing Network Design Spaces},
  author = {Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Doll{'a}r},
  booktitle = {CVPR},
  year = {2020}
}

bibtex
@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}}
}

Files & Weights

Filename	Size	Action
model.safetensors	0.04 GB
pytorch_model.bin	0.04 GB

timm/regnety_016.tv2_in1k

Model Documentation

Model card for regnety_016.tv2_in1k

Model Details

Model Usage

Image Classification

get model specific transforms (normalization, resize)

unsqueeze single image into batch of 1

Feature Map Extraction

get model specific transforms (normalization, resize)

unsqueeze single image into batch of 1

print shape of each feature map in output

e.g.:

torch.Size([1, 32, 112, 112])

torch.Size([1, 48, 56, 56])

torch.Size([1, 120, 28, 28])

torch.Size([1, 336, 14, 14])

torch.Size([1, 888, 7, 7])

Image Embeddings

remove classifier nn.Linear

get model specific transforms (normalization, resize)

output is (batch_size, num_features) shaped tensor

or equivalently (without needing to set num_classes=0)

output is unpooled, a (1, 888, 7, 7) shaped tensor

output is a (1, num_features) shaped tensor

Model Comparison

Citation

Files & Weights

Access Download