

# ResNet-34 model

Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ResNet architecture was introduced in [this paper](https://arxiv.org/pdf/1512.03385.pdf).


## Model description

The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.


## Installation

### Prerequisites

Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.

### Latest stable release

You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:

```shell
pip install pylocron
```

or using [conda](https://anaconda.org/frgfm/pylocron):

```shell
conda install -c frgfm pylocron
```

### Developer mode

Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:

```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```


## Usage instructions

```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub

model = model_from_hf_hub("frgfm/resnet34").eval()

img = Image.open(path_to_an_image).convert("RGB")

# Preprocessing
config = model.default_cfg
transform = Compose([
    Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
    PILToTensor(),
    ConvertImageDtype(torch.float32),
    Normalize(config['mean'], config['std'])
])

input_tensor = transform(img).unsqueeze(0)

# Inference
with torch.inference_mode():
    output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```


## Citation

Original paper

```bibtex
@article{DBLP:journals/corr/HeZRS15,
  author    = {Kaiming He and
               Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
  title     = {Deep Residual Learning for Image Recognition},
  journal   = {CoRR},
  volume    = {abs/1512.03385},
  year      = {2015},
  url       = {http://arxiv.org/abs/1512.03385},
  eprinttype = {arXiv},
  eprint    = {1512.03385},
  timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

Source of this implementation

```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```

{"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]}
transformers pytorch onnx image-classification dataset:frgfm/imagenette arxiv:1512.03385 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us 



# ResNet-34 model

Pretrained on ImageNette. The ResNet architecture was introduced in this paper.


## Model description

The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.


## Installation

### Prerequisites

Python 3.6 (or higher) and pip/conda are required to install Holocron.

### Latest stable release

You can install the last stable release of the package using pypi as follows:



or using conda:



### Developer mode

Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install Git first)*:




## Usage instructions




Original paper



Source of this implementation



# ResNet-34 model

Pretrained on ImageNette. The ResNet architecture was introduced in this paper. ## Model description

The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection. ## Installation ### Prerequisites

Python 3.6 (or higher) and pip/conda are required to install Holocron. ### Latest stable release

You can install the last stable release of the package using pypi as follows:



or using conda: ### Developer mode

Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install Git first)*: ## Usage instructions




Original paper



Source of this implementation
TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us 
 # ResNet-34 model

Pretrained on ImageNette. The ResNet architecture was introduced in this paper. ## Model description

The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection. ## Installation ### Prerequisites

Python 3.6 (or higher) and pip/conda are required to install Holocron. ### Latest stable release

You can install the last stable release of the package using pypi as follows:



or using conda: ### Developer mode

Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install Git first)*: ## Usage instructions




Original paper



Source of this implementation
passage: TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us 
# ResNet-34 model

Pretrained on ImageNette. The ResNet architecture was introduced in this paper.## Model description

The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.## Installation### Prerequisites

Python 3.6 (or higher) and pip/conda are required to install Holocron.### Latest stable release

You can install the last stable release of the package using pypi as follows:



or using conda:### Developer mode

Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install Git first)*:## Usage instructions




Original paper



Source of this implementation