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library_name: litert
pipeline_tag: image-classification
tags:
- vision
- image-classification
- google
- computer-vision
datasets:
- imagenet-1k
base_model:
- google/efficientnet-b2
model-index:
- name: EfficientNet_B2
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: 0.8061
- name: Top 5 Accuracy (Full Precision)
type: accuracy
value: 0.9530
- name: Top 1 Accuracy (Dynamic Quantized wi8 afp32)
type: accuracy
value: 0.8012
- name: Top 5 Accuracy (Dynamic Quantized wi8 afp32)
type: accuracy
value: 0.9501
---
# EfficientNet B2
EfficientNet B2 model pre-trained on ImageNet-1k. Originally introduced by Tan and Le in the influential paper,[ **EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks**](https://arxiv.org/abs/1905.11946) this model utilizes compound scaling to systematically balance network depth, width, and resolution, enabling superior accuracy with significantly higher efficiency than traditional architectures.
## Model description
The model was converted from a checkpoint from PyTorch Vision.
The original model has:
acc@1 (on ImageNet-1K): 80.608%
acc@5 (on ImageNet-1K): 95.31%
num_params: 9,109,994
## 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.
## Use
```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 = 288
if w < h:
img = img.resize((s, int(round(h * s / w))), Image.BICUBIC)
else:
img = img.resize((int(round(w * s / h)), s), Image.BICUBIC)
left = (img.size[0] - 288) // 2
top = (img.size[1] - 288) // 2
img = img.crop((left, top, left + 288, top + 288))
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.transpose(x, (2, 0, 1))
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--image", required=True)
args = ap.parse_args()
model_path = hf_hub_download("litert-community/efficientnet_b2", "efficientnet_b2.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()
```
### BibTeX entry and citation info
```bibtex
@article{Tan2019EfficientNetRM,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Mingxing Tan and Quoc V. Le},
journal={ArXiv},
year={2019},
volume={abs/1905.11946}
}
```
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