File size: 4,578 Bytes
33b51de 307145f 33b51de 307145f 33b51de 307145f 33b51de 307145f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | ---
library_name: litert
pipeline_tag: image-classification
tags:
- vision
- image-classification
- google
- computer-vision
datasets:
- imagenet-1k
model-index:
- name: litert-community/vgg19
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: 0.7238
- name: Top 5 Accuracy (Full Precision)
type: accuracy
value: 0.9089
---
# VGG19
VGG19 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**](https://arxiv.org/abs/1409.1556) this 19-layer architecture represents the deepest configuration of the VGG series, utilizing an extensive stack of \\(3 \times 3 \\) convolutional layers to extract increasingly complex features and achieve high accuracy on large-scale visual recognition tasks.
## Model description
The model was converted from a checkpoint from PyTorch Vision.
The original model has:
acc@1 (on ImageNet-1K): 72.376%
acc@5 (on ImageNet-1K): 90.876%
num_params: 143667240
## 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(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/vgg19", "vgg19.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
@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},
}
``` |