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MobileNetV4_Conv_Large_TFLite_384

Image Classification
timm
TF Lite
MobileNetV4
Model card Files Community
MobileNetV4_Conv_Large_TFLite_384
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---
tags:
- image-classification
- timm
- MobileNetV4
license: apache-2.0
datasets:
- imagenet-1k
pipeline_tag: image-classification
---
# Model card for MobileNetV4_Conv_Large_TFLite_384

A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman.

Converted to TFLite Float32 & Float16 formats by Youssef Boulaouane.


## Model Details
- **Pytorch Weights:** https://huggingface.co/timm/mobilenetv4_conv_large.e600_r384_in1k
- **Model Type:** Image classification
- **Model Stats:**
  - Params (M): 32.6
  - GMACs: 6.4
  - Activations (M): 27.3
  - Input Shape (1, 384, 384, 3) 
- **Dataset:** ImageNet-1k
- **Papers:**
  - MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518
  - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
- **Original:** https://github.com/tensorflow/models/tree/master/official/vision

## Model Usage
### Image Classification in Python
```python
import numpy as np
import tensorflow as tf
from PIL import Image

# Load label file
with open('imagenet_classes.txt', 'r') as file:
    lines = file.readlines()

index_to_label = {index: line.strip() for index, line in enumerate(lines)}

# Initialize interpreter and IO details
tfl_model = tf.lite.Interpreter(model_path=tf_model_path)
tfl_model.allocate_tensors()
input_details = tfl_model.get_input_details()
output_details = tfl_model.get_output_details()

# Load and preprocess the image
image = Image.open(image_path).resize((384, 384), Image.BICUBIC)

image = np.array(image, dtype=np.float32)
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
image = (image / 255.0 - mean) / std

image = np.expand_dims(image, axis=-1)
image = np.rollaxis(image, 3)

# Inference and postprocessing
input = input_details[0]
tfl_model.set_tensor(input["index"], image)
tfl_model.invoke()

tfl_output = tfl_model.get_tensor(output_details[0]["index"])
tfl_output_tensor = tf.convert_to_tensor(tfl_output)
tfl_softmax_output = tf.nn.softmax(tfl_output_tensor, axis=1)

tfl_top5_probs, tfl_top5_indices = tf.math.top_k(tfl_softmax_output, k=5)

# Get the top5 class labels and probabilities
tfl_probs_list = tfl_top5_probs[0].numpy().tolist()
tfl_index_list = tfl_top5_indices[0].numpy().tolist()

for index, prob in zip(tfl_index_list, tfl_probs_list):
    print(f"{index_to_label[index]}: {round(prob*100, 2)}%")
```

### Deployment on Mobile
Refer to guides available here: https://ai.google.dev/edge/lite/inference

## Citation
```bibtex
@article{qin2024mobilenetv4,
  title={MobileNetV4-Universal Models for the Mobile Ecosystem},
  author={Qin, Danfeng and Leichner, Chas and Delakis, Manolis and Fornoni, Marco and Luo, Shixin and Yang, Fan and Wang, Weijun and Banbury, Colby and Ye, Chengxi and Akin, Berkin and others},
  journal={arXiv preprint arXiv:2404.10518},
  year={2024}
}
```
```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}}
}
```