mobilenetv3 small 100.lamb in1k

Providertimm
Categoryimage-classification
Licenseapache-2.0
Downloads2.2K
Stars4

Overview

MobileNetV3-Small is a lightweight convolutional neural network optimized for low-latency inference on mobile and edge devices. Unlike larger vision models, it utilizes hardware-aware architecture search and NetAdapt to balance accuracy with computational efficiency. This specific variant, trained on ImageNet-1k via the timm library, is ideal for developers building real-time image classification pipelines where RAM and CPU cycles are strictly limited. It integrates seamlessly into PyTorch workflows, offering a high-performance alternative to heavier backbones when deploying to Android, iOS, or embedded IoT hardware.

Highlights

  • Optimized for low-latency edge and mobile deployment
  • Efficient ImageNet-1k classification via timm integration
  • Hardware-aware architecture reduces computational overhead
  • Apache-2.0 license for flexible commercial production use

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/mobilenetv3_small_100.lamb_in1k")
tokenizer = AutoTokenizer.from_pretrained("timm/mobilenetv3_small_100.lamb_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

Download the full repository 
huggingface-cli download timm/mobilenetv3_small_100.lamb_in1k

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

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download timm/mobilenetv3_small_100.lamb_in1k config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('timm/mobilenetv3_small_100.lamb_in1k')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/timm/mobilenetv3_small_100.lamb_in1k

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/timm/mobilenetv3_small_100.lamb_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

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('timm/mobilenetv3_small_100.lamb_in1k')
tokenizer = AutoTokenizer.from_pretrained('timm/mobilenetv3_small_100.lamb_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

Download the full repository 
modelscope download --model timm/mobilenetv3_small_100.lamb_in1k

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

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model timm/mobilenetv3_small_100.lamb_in1k README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('timm/mobilenetv3_small_100.lamb_in1k')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/timm/mobilenetv3_small_100.lamb_in1k.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/timm/mobilenetv3_small_100.lamb_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

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

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'timm/mobilenetv3_small_100.lamb_in1k')

Full Documentation

来源: HuggingFace

---
tags:

  • image-classification

  • timm

  • transformers

library_name: timm
license: apache-2.0
datasets:
  • imagenet-1k

---

Model card for mobilenetv3_small_100.lamb_in1k

A MobileNet-v3 image classification model. Trained on ImageNet-1k in timm using recipe template described below.

Recipe details:
* A LAMB optimizer based recipe that is similar to ResNet Strikes Back A2 but 50% longer with EMA weight averaging, no CutMix
* Step (exponential decay w/ staircase) LR schedule with warmup

Model Details

  • Model Type: Image classification / feature backbone
  • Model Stats:
- Params (M): 2.5 - GMACs: 0.1 - Activations (M): 1.4 - Image size: 224 x 224
  • Papers:
- Searching for MobileNetV3: https://arxiv.org/abs/1905.02244
  • Dataset: ImageNet-1k
  • Original: https://github.com/huggingface/pytorch-image-models

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('mobilenetv3_small_100.lamb_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(
    'mobilenetv3_small_100.lamb_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, 16, 112, 112])
    #  torch.Size([1, 16, 56, 56])
    #  torch.Size([1, 24, 28, 28])
    #  torch.Size([1, 48, 14, 14])
    #  torch.Size([1, 576, 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(
    'mobilenetv3_small_100.lamb_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, 576, 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.

Citation

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}}
}
bibtex
@inproceedings{howard2019searching,
  title={Searching for mobilenetv3},
  author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={1314--1324},
  year={2019}
}
View official source
Join our Telegram