Name: resnet50.a1 in1k
Author: timm

Overview

The resnet50.a1_in1k is a refined implementation of the classic ResNet-50 architecture, optimized for image classification tasks. Based on the timm library, this variant leverages improved training recipes and weight initialization to achieve better accuracy on the ImageNet-1K dataset compared to the original baseline. For developers, it serves as a highly reliable feature extractor or a starting point for transfer learning in computer vision pipelines. Its balanced parameter count and proven stability make it an ideal choice for production environments where inference latency and memory overhead must be strictly managed without sacrificing significant predictive power.

Highlights

Optimized ImageNet-1K weights for superior baseline accuracy
Seamless integration via the timm library ecosystem
Ideal for transfer learning and feature extraction
Balanced performance between inference speed and precision
Permissive Apache-2.0 license for commercial deployment

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("timm/resnet50.a1_in1k")
tokenizer = AutoTokenizer.from_pretrained("timm/resnet50.a1_in1k")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download timm/resnet50.a1_in1k

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download timm/resnet50.a1_in1k config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('timm/resnet50.a1_in1k')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/timm/resnet50.a1_in1k

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/timm/resnet50.a1_in1k

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('timm/resnet50.a1_in1k')
tokenizer = AutoTokenizer.from_pretrained('timm/resnet50.a1_in1k')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model timm/resnet50.a1_in1k

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model timm/resnet50.a1_in1k README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('timm/resnet50.a1_in1k')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/timm/resnet50.a1_in1k.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/timm/resnet50.a1_in1k.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'timm/resnet50.a1_in1k')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
library_name: timm
tags:

image-classification

timm

transformers

---

Model card for resnet50.a1_in1k

A ResNet-B image classification model.

This model features:
* ReLU activations
* single layer 7x7 convolution with pooling
* 1x1 convolution shortcut downsample

Trained on ImageNet-1k in timm using recipe template described below.

Recipe details:
* ResNet Strikes Back A1 recipe
* LAMB optimizer with BCE loss
* Cosine LR schedule with warmup

Model Details

Model Type: Image classification / feature backbone

Model Stats:

- Params (M): 25.6 - GMACs: 4.1 - Activations (M): 11.1 - Image size: train = 224 x 224, test = 288 x 288

Papers:

- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385

Original: https://github.com/huggingface/pytorch-image-models

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('resnet50.a1_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(
    'resnet50.a1_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, 112, 112])
    #  torch.Size([1, 256, 56, 56])
    #  torch.Size([1, 512, 28, 28])
    #  torch.Size([1, 1024, 14, 14])
    #  torch.Size([1, 2048, 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(
    'resnet50.a1_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, 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.

|model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec|
|------------------------------------------|--------|-----|-----|-----------|-----|-----|-------|
|seresnextaa101d_32x8d.sw_in12k_ft_in1k_288|320 |86.72|98.17|93.6 |35.2 |69.7 |451 |
|seresnextaa101d_32x8d.sw_in12k_ft_in1k_288|288 |86.51|98.08|93.6 |28.5 |56.4 |560 |
|seresnextaa101d_32x8d.sw_in12k_ft_in1k|288 |86.49|98.03|93.6 |28.5 |56.4 |557 |
|seresnextaa101d_32x8d.sw_in12k_ft_in1k|224 |85.96|97.82|93.6 |17.2 |34.2 |923 |
|resnext101_32x32d.fb_wsl_ig1b_ft_in1k|224 |85.11|97.44|468.5 |87.3 |91.1 |254 |
|resnetrs420.tf_in1k|416 |85.0 |97.12|191.9 |108.4|213.8|134 |
|ecaresnet269d.ra2_in1k|352 |84.96|97.22|102.1 |50.2 |101.2|291 |
|ecaresnet269d.ra2_in1k|320 |84.73|97.18|102.1 |41.5 |83.7 |353 |
|resnetrs350.tf_in1k|384 |84.71|96.99|164.0 |77.6 |154.7|183 |
|seresnextaa101d_32x8d.ah_in1k|288 |84.57|97.08|93.6 |28.5 |56.4 |557 |
|resnetrs200.tf_in1k|320 |84.45|97.08|93.2 |31.5 |67.8 |446 |
|resnetrs270.tf_in1k|352 |84.43|96.97|129.9 |51.1 |105.5|280 |
|seresnext101d_32x8d.ah_in1k|288 |84.36|96.92|93.6 |27.6 |53.0 |595 |
|seresnet152d.ra2_in1k|320 |84.35|97.04|66.8 |24.1 |47.7 |610 |
|resnetrs350.tf_in1k|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 |
|resnext101_32x8d.fb_swsl_ig1b_ft_in1k|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 |
|resnetrs420.tf_in1k|320 |84.24|96.86|191.9 |64.2 |126.6|228 |
|seresnext101_32x8d.ah_in1k|288 |84.19|96.87|93.6 |27.2 |51.6 |613 |
|resnext101_32x16d.fb_wsl_ig1b_ft_in1k|224 |84.18|97.19|194.0 |36.3 |51.2 |581 |
|resnetaa101d.sw_in12k_ft_in1k|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 |
|resnet200d.ra2_in1k|320 |83.97|96.82|64.7 |31.2 |67.3 |518 |
|resnetrs200.tf_in1k|256 |83.87|96.75|93.2 |20.2 |43.4 |692 |
|seresnextaa101d_32x8d.ah_in1k|224 |83.86|96.65|93.6 |17.2 |34.2 |923 |
|resnetrs152.tf_in1k|320 |83.72|96.61|86.6 |24.3 |48.1 |617 |
|seresnet152d.ra2_in1k|256 |83.69|96.78|66.8 |15.4 |30.6 |943 |
|seresnext101d_32x8d.ah_in1k|224 |83.68|96.61|93.6 |16.7 |32.0 |986 |
|resnet152d.ra2_in1k|320 |83.67|96.74|60.2 |24.1 |47.7 |706 |
|resnetrs270.tf_in1k|256 |83.59|96.61|129.9 |27.1 |55.8 |526 |
|seresnext101_32x8d.ah_in1k|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 |
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