--- library_name: litert pipeline_tag: image-classification tags: - vision - image-classification - google - computer-vision datasets: - imagenet-1k model-index: - name: litert-community/MobileNet-v3-small results: - task: type: image-classification name: Image Classification dataset: name: ImageNet-1k type: imagenet-1k config: default split: validation metrics: - name: Top 1 Accuracy (Full Precision) type: accuracy value: 0.6762 - name: Top 5 Accuracy (Full Precision) type: accuracy value: 0.8740 --- # MobileNet V3 Small MobileNet V3 model pre-trained on ImageNet-1k at resolution 224x224. Originally introduced by Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, and Hartwig Adam in the paper, [**Searching for MobileNetV3**](https://arxiv.org/abs/1905.02244). ## Model description The model was converted from a checkpoint from PyTorch Vision. The original model has: acc@1 (on ImageNet-1K): 67.668% acc@5 (on ImageNet-1K): 87.402% num_params: 2,542,856 The license information of the original model was missing. ## Static INT8 status: work in progress Static INT8 quantization is not available because naive post-training quantization causes severe classification accuracy degradation. Naive post-training quantization of this MobileNetV3 Small checkpoint can lose important activation detail: channels with very different ranges share one INT8 activation scale, and the first depthwise convolution amplifies the resulting rounding error. Successful conversion or NPU compilation does not establish acceptable accuracy. We are working on a validated solution. **In the meantime, please use a larger model, such as [MobileNetV3 Large](https://huggingface.co/litert-community/MobileNet-v3-large), when you need static INT8 inference.** No validated static INT8 MobileNetV3 Small checkpoint is currently available in this repository. The FP32 and weight-only INT8 variants listed below are available. ## Available model files | File | Description | | --- | --- | | `mobilenet_v3_small.tflite` | Full precision LiteRT/TFLite model. | | `mobilenet_v3_small_weight_only_wi8_afp32.tflite` | Weight-only INT8 model with FP32 activations. | | `mobilenet_v3_small_Google_Tensor_G5_apply_plugin.tflite` | AOT-compiled artifact for the Google Tensor G5 target. | ### Quantized variant `mobilenet_v3_small_weight_only_wi8_afp32.tflite` is a weight-only int8 quantization of the same weights (about 3.7x smaller than float32). Weight-only quantization is used instead of dynamic-range quantization because MobileNetV3's SE and hard-swish layers are sensitive to activation quantization; in a spot check against the float model the weight-only file keeps the top-1 predictions on real photos with a minimum logit correlation of 0.991. ## Compatibility | File | CPU | GPU | NPU | |---|---|---|---| | `mobilenet_v3_small.tflite` | Supported | Supported | N/A | For GPU execution, explicitly select FP32 precision. ## Intended uses & limitations The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case. ## How to Use ​​**1. Install Dependencies** Ensure your Python environment is set up with the required libraries. Run the following command in your terminal: ```bash pip install numpy Pillow huggingface_hub ai-edge-litert ``` **2. Prepare Your Image** The script expects an image file to analyze. Make sure you have an image (e.g., cat.jpg or car.png) saved in the same working directory as your script. **3. Save the Script** Create a new file named `classify.py`, paste the script below into it, and save the file: ```python #!/usr/bin/env python3 import argparse, json import numpy as np from PIL import Image from huggingface_hub import hf_hub_download from ai_edge_litert.compiled_model import CompiledModel def preprocess(img: Image.Image) -> np.ndarray: img = img.convert("RGB") w, h = img.size s = 256 if w < h: img = img.resize((s, int(h * s / w)), Image.BILINEAR) else: img = img.resize((int(w * s / h), s), Image.BILINEAR) left = int(round((img.size[0] - 224) / 2.0)) top = int(round((img.size[1] - 224) / 2.0)) img = img.crop((left, top, left + 224, top + 224)) x = np.asarray(img, dtype=np.float32) / 255.0 x = (x - np.array([0.485, 0.456, 0.406], dtype=np.float32)) / np.array( [0.229, 0.224, 0.225], dtype=np.float32 ) return np.ascontiguousarray(x.transpose(2, 0, 1)[None]) def main(): ap = argparse.ArgumentParser() ap.add_argument("--image", required=True) args = ap.parse_args() model_path = hf_hub_download("litert-community/MobileNet-v3-small", "mobilenet_v3_small.tflite") labels_path = hf_hub_download( "huggingface/label-files", "imagenet-1k-id2label.json", repo_type="dataset" ) with open(labels_path, "r", encoding="utf-8") as f: id2label = {int(k): v for k, v in json.load(f).items()} img = Image.open(args.image) x = preprocess(img) model = CompiledModel.from_file(model_path) inp = model.create_input_buffers(0) out = model.create_output_buffers(0) inp[0].write(x) model.run_by_index(0, inp, out) req = model.get_output_buffer_requirements(0, 0) y = out[0].read(req["buffer_size"] // np.dtype(np.float32).itemsize, np.float32) pred = int(np.argmax(y)) label = id2label.get(pred, f"class_{pred}") print(f"Top-1 class index: {pred}") print(f"Top-1 label: {label}") if __name__ == "__main__": main() ``` **4. Execute the Python Script** Run the below command: ```bash python classify.py --image cat.jpg ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1905-02244, author = {Andrew Howard and Mark Sandler and Grace Chu and Liang{-}Chieh Chen and Bo Chen and Mingxing Tan and Weijun Wang and Yukun Zhu and Ruoming Pang and Vijay Vasudevan and Quoc V. Le and Hartwig Adam}, title = {Searching for MobileNetV3}, journal = {CoRR}, volume = {abs/1905.02244}, year = {2019}, url = {http://arxiv.org/abs/1905.02244}, eprinttype = {arXiv}, eprint = {1905.02244}, timestamp = {Thu, 27 May 2021 16:20:51 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```