library_name: litert
pipeline_tag: image-classification
tags:
- vision
- image-classification
- google
- computer-vision
datasets:
- imagenet-1k
model-index:
- name: litert-community/vgg16
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: 0.7161
- name: Top 5 Accuracy (Full Precision)
type: accuracy
value: 0.9038
VGG16
VGG16 model pre-trained on ImageNet-1k. Originally introduced by Karen Simonyan and Andrew Zisserman in the influential paper, Very Deep Convolutional Networks for Large-Scale Image Recognition this 16-layer architecture popularized the use of small filters stacked in blocks, proving that increasing depth is a primary driver for improved visual representation and accuracy in large-scale recognition tasks.
Model description
The model was converted from a checkpoint from PyTorch Vision.
The original model has:
acc@1 (on ImageNet-1K): 71.592%
acc@5 (on ImageNet-1K): 90.382%
num_params: 138357544
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
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
#!/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(round(h * s / w))), Image.BILINEAR)
else:
img = img.resize((int(round(w * s / h)), s), Image.BILINEAR)
left = (img.size[0] - 224) // 2
top = (img.size[1] - 224) // 2
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.expand_dims(x, axis=0)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--image", required=True)
args = ap.parse_args()
model_path = hf_hub_download("litert-community/vgg16", "vgg16.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
@misc{simonyan2015deepconvolutionalnetworkslargescale,
title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
author={Karen Simonyan and Andrew Zisserman},
year={2015},
eprint={1409.1556},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/1409.1556},
}