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--- |
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tags: |
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- image-classification |
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- timm |
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- MobileNetV4 |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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pipeline_tag: image-classification |
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--- |
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# Model card for MobileNetV4_Conv_Medium_TFLite_256 |
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A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman. |
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Converted to TFLite Float32 & Float16 formats by Youssef Boulaouane. |
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## Model Details |
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- **Pytorch Weights:** https://huggingface.co/timm/mobilenetv4_conv_medium.e500_r256_in1k |
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- **Model Type:** Image classification |
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- **Model Stats:** |
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- Params (M): 9.7 |
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- GMACs: 1.1 |
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- Activations (M): 7.6 |
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- Input Shape (1, 256, 256, 3) |
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- **Dataset:** ImageNet-1k |
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- **Papers:** |
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- MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518 |
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- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models |
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- **Original:** https://github.com/tensorflow/models/tree/master/official/vision |
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## Model Usage |
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### Image Classification in Python |
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```python |
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import numpy as np |
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import tensorflow as tf |
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from PIL import Image |
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# Load label file |
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with open('imagenet_classes.txt', 'r') as file: |
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lines = file.readlines() |
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index_to_label = {index: line.strip() for index, line in enumerate(lines)} |
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# Initialize interpreter and IO details |
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tfl_model = tf.lite.Interpreter(model_path=tf_model_path) |
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tfl_model.allocate_tensors() |
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input_details = tfl_model.get_input_details() |
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output_details = tfl_model.get_output_details() |
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# Load and preprocess the image |
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image = Image.open(image_path).resize((256, 256), Image.BICUBIC) |
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image = np.array(image, dtype=np.float32) |
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mean = np.array([0.485, 0.456, 0.406], dtype=np.float32) |
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std = np.array([0.229, 0.224, 0.225], dtype=np.float32) |
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image = (image / 255.0 - mean) / std |
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image = np.expand_dims(image, axis=-1) |
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image = np.rollaxis(image, 3) |
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# Inference and postprocessing |
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input = input_details[0] |
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tfl_model.set_tensor(input["index"], image) |
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tfl_model.invoke() |
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tfl_output = tfl_model.get_tensor(output_details[0]["index"]) |
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tfl_output_tensor = tf.convert_to_tensor(tfl_output) |
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tfl_softmax_output = tf.nn.softmax(tfl_output_tensor, axis=1) |
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tfl_top5_probs, tfl_top5_indices = tf.math.top_k(tfl_softmax_output, k=5) |
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# Get the top5 class labels and probabilities |
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tfl_probs_list = tfl_top5_probs[0].numpy().tolist() |
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tfl_index_list = tfl_top5_indices[0].numpy().tolist() |
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for index, prob in zip(tfl_index_list, tfl_probs_list): |
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print(f"{index_to_label[index]}: {round(prob*100, 2)}%") |
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``` |
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### Deployment on Mobile |
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Refer to guides available here: https://ai.google.dev/edge/lite/inference |
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## Citation |
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```bibtex |
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@article{qin2024mobilenetv4, |
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title={MobileNetV4-Universal Models for the Mobile Ecosystem}, |
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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}, |
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journal={arXiv preprint arXiv:2404.10518}, |
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year={2024} |
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} |
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``` |
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```bibtex |
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@misc{rw2019timm, |
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author = {Ross Wightman}, |
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title = {PyTorch Image Models}, |
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year = {2019}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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doi = {10.5281/zenodo.4414861}, |
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howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} |
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} |
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``` |