Create README.md

README.md

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+---
+tags:
+- image-classification
+- timm
+- TFLite
+- MobileNetV4
+license: apache-2.0
+datasets:
+- imagenet-1k
+pipeline_tag: image-classification
+---
+# Model card for MobileNetV4_Conv_Small_TFLite_224
+
+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_small.e2400_r224_in1k
+- **Model Type:** Image classification
+- **Model Stats:**
+  - Params (M): 3.8
+  - GMACs: 0.2
+  - Activations (M): 2.0
+  - Image size: train = 224 x 224, test = 256 x 256
+- **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 tensorflow as tf
+
+# 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
+mean = [0.485, 0.456, 0.406]
+std = [0.229, 0.224, 0.225]
+
+image = tf.image.resize(
+    tf.image.decode_image(tf.io.read_file(image_path), channels=3),
+    [256, 256],
+    method="bicubic",
+)
+image = tf.image.central_crop(image, central_fraction=224.0/256.0)
+image = (image - mean) / std
+
+# 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)
+```
+
+### 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}}
+}
+```