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litert-community/
squeezenet1_0

Name: squeezenet1_0
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Squeezenet1_0

SqueezeNet 1.0 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size by Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer.

Model description

The model was converted from a checkpoint from PyTorch Vision (SqueezeNet1_0_Weights.IMAGENET1K_V1).

The original model has:
acc@1 (on ImageNet-1K): 58.092%
acc@5 (on ImageNet-1K): 80.420%
num_params: 1,248,424

This model is released under the BSD 3-Clause License, inheriting the license of the torchvision repository from which it was converted.

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.

The preprocessing script below has been adjusted to handle standard ImageNet resize (256) and central crop (224) requirements. It explicitly strips away the legacy PyTorch (B, C, H, W) layout and adds the required Batch dimension to match the LiteRT (B, H, W, C) (NHWC) runtime expectation.

How to Use

1. Install Dependencies

Ensure your Python environment is set up with the required libraries. Run the following command in your terminal

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
import 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

   # Resize shortest edge to 256
   s = 256
   if w < h:
       img = img.resize((s, int(round(h * s / w))), Image.BILINEAR)
   else:
       img = img.resize((int(round(w * s / h)), s), Image.BILINEAR)

   # Central crop to 224x224
   left = (img.size[0] - 224) // 2
   top = (img.size[1] - 224) // 2
   img = img.crop((left, top, left + 224, top + 224))

   # Rescale to [0.0, 1.0] and Normalize
   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
   )

   # Expand dimensions to create NHWC 4D tensor: (1, 224, 224, 3)
   x = np.expand_dims(x, axis=0)

   return x

def main():
   ap = argparse.ArgumentParser()
   ap.add_argument("--image", required=True, help="Path to the input image")
   args = ap.parse_args()

   # Download the TFLite model and labels
   model_path = hf_hub_download("litert-community/squeezenet1_0", "squeezenet1_0.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:

python classify.py --image cat.jpg

BibTeX entry and citation info

bibtex

@misc{iandola2016squeezenetalexnetlevelaccuracy50x,
      title={SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size}, 
      author={Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer},
      year={2016},
      eprint={1602.07360},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1602.07360}, 
}