Create README.md

README.md

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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_Medium_TFLite_256
+
+A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman.
+
+Converted to TFLite Float32 & Float16 formats by Youssef Boulaouane.
+
+_**Note from original pytorch model:** This is a customization with blur pooling for downsample convs after the stem._
+
+
+## Model Details
+- **Pytorch Weights:** https://huggingface.co/timm/mobilenetv4_conv_medium.e500_r256_in1k
+- **Model Type:** Image classification
+- **Model Stats:**
+  - Params (M): 9.7
+  - GMACs: 1.1
+  - Activations (M): 7.6
+  - Input Shape (1, 256, 256, 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((256, 256), 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}}
+}
+```