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naver-ai/
rdnet_large.nv_in1k_ft_in1k_384

Name: rdnet_large.nv_in1k_ft_in1k_384
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Model card for rdnet_large.nv_in1k_ft_in1k_384

A RDNet image classification model. Trained on ImageNet-1k, original torchvision weights.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Imagenet-1k validation top-1 accuracy: 85.8%
- Params (M): 186
- GMACs: 101.9
- Image size: 384 x 384
Papers:
- DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs: https://arxiv.org/abs/2403.19588
Dataset: ImageNet-1k

Model Usage

Image Classification

python

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

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

model = timm.create_model('rdnet_large.nv_in1k_ft_in1k_384', 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(
    'rdnet_large.nv_in1k_ft_in1k_384',
    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, 528, 96, 96])
    # torch.Size([1, 840, 48, 48])
    # torch.Size([1, 1528, 24, 24])
    # torch.Size([1, 2000, 12, 12])

    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(
    'rdnet_large.nv_in1k_ft_in1k_384',
    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, 2000, 12, 12) shaped tensor

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

Citation

@misc{kim2024densenets,
    title={DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs}, 
    author={Donghyun Kim and Byeongho Heo and Dongyoon Han},
    year={2024},
    eprint={2403.19588},
    archivePrefix={arXiv},
}

naver-ai/rdnet_large.nv_in1k_ft_in1k_384

Model card for rdnet_large.nv_in1k_ft_in1k_384

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Citation

naver-ai/
rdnet_large.nv_in1k_ft_in1k_384