---
title: resnetv2_101x1_bit.goog_in21k_ft_in1k
canonical_url: "https://www.modelscope.cn/models/timmmodel/resnetv2_101x1_bit.goog_in21k_ft_in1k"
md_url: "https://www.modelscope.cn/models/timmmodel/resnetv2_101x1_bit.goog_in21k_ft_in1k.md"
repository: timmmodel/resnetv2_101x1_bit.goog_in21k_ft_in1k
last_updated: 2026-08-18
license: apache-2.0
parameters: 44.5M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 5
stars: 0
tags:
  - image-classification
  - timm
  - transformers
---

# resnetv2_101x1_bit.goog_in21k_ft_in1k

> resnetv2_101x1_bit.goog_in21k_ft_in1k - timmmodel 在 ModelScope 开源的模型。Model card for resnetv2101x1bit.googin21kftin1k

timmmodel/resnetv2_101x1_bit.goog_in21k_ft_in1k 是 ModelScope 魔搭社区上的 44.5M 参数机器学习模型，采用 apache-2.0 许可。

- **Repository**: timmmodel/resnetv2_101x1_bit.goog_in21k_ft_in1k
- **License**: apache-2.0
- **Parameters**: 44.5M
- **Tags**: image-classification, timm, transformers
- **Downloads**: 5
- **Stars**: 0
- **Last updated**: 2026-08-18

Source: https://www.modelscope.cn/models/timmmodel/resnetv2_101x1_bit.goog_in21k_ft_in1k

---

# Model card for resnetv2_101x1_bit.goog_in21k_ft_in1k

A ResNet-V2-BiT (Big Transfer w/ pre-activation ResNet) image classification model. Pretrained on ImageNet-21k and fine-tuned on ImageNet-1k by paper authors.

This model uses:
* Group Normalization (GN) in combination with Weight Standardization (WS) instead of Batch Normalization (BN)..


## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 44.5
  - GMACs: 31.7
  - Activations (M): 64.9
  - Image size: 448 x 448
- **Papers:**
  - Big Transfer (BiT): General Visual Representation Learning: https://arxiv.org/abs/1912.11370
  - Identity Mappings in Deep Residual Networks: https://arxiv.org/abs/1603.05027
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-21k
- **Original:** https://github.com/google-research/big_transfer

## 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('resnetv2_101x1_bit.goog_in21k_ft_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(
    'resnetv2_101x1_bit.goog_in21k_ft_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, 224, 224])
    #  torch.Size([1, 256, 112, 112])
    #  torch.Size([1, 512, 56, 56])
    #  torch.Size([1, 1024, 28, 28])
    #  torch.Size([1, 2048, 14, 14])

    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(
    'resnetv2_101x1_bit.goog_in21k_ft_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, 14, 14) 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](https://github.com/huggingface/pytorch-image-models/tree/main/results).

## Citation
```bibtex
@inproceedings{Kolesnikov2019BigT,
  title={Big Transfer (BiT): General Visual Representation Learning},
  author={Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Joan Puigcerver and Jessica Yung and Sylvain Gelly and Neil Houlsby},
  booktitle={European Conference on Computer Vision},
  year={2019}
}
```
```bibtex
@article{He2016,
  author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
  title = {Identity Mappings in Deep Residual Networks},
  journal = {arXiv preprint arXiv:1603.05027},
  year = {2016}
}
```
```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}}
}
```