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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_Large_TFLite_256
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+
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+ A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman.
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+
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+ Converted to TFLite Float32 & Float16 formats by Youssef Boulaouane.
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+
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+
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+ ## Model Details
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+ - **Pytorch Weights:** https://huggingface.co/timm/mobilenetv4_conv_large.e500_r256_in1k
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+ - **Model Type:** Image classification
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+ - **Model Stats:**
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+ - Params (M): 32.6
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+ - GMACs: 2.9
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+ - Activations (M): 12.1
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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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+
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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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+
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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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+
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+ index_to_label = {index: line.strip() for index, line in enumerate(lines)}
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+
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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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+
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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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+
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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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+
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+ image = np.expand_dims(image, axis=-1)
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+ image = np.rollaxis(image, 3)
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+
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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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+
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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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+
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+ tfl_top5_probs, tfl_top5_indices = tf.math.top_k(tfl_softmax_output, k=5)
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+
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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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+
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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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+
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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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+
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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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+ ```