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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-b1
model-index:
- name: litert-community/efficientnet_b1
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: 0.7855
- name: Top 5 Accuracy (Full Precision)
type: accuracy
value: 0.9419
- name: Top 1 Accuracy (Dynamic Quantized wi8 afp32)
type: accuracy
value: 0.7805
- name: Top 5 Accuracy (Dynamic Quantized wi8 afp32)
type: accuracy
value: 0.9392
---
# EfficientNet B1
EfficientNet B1 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 original model has:
acc@1 (on ImageNet-1K): 79.838%
acc@5 (on ImageNet-1K): 94.934%
num_params: 7,794,184
## 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 = 255
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] - 240) // 2
top = (img.size[1] - 240) // 2
img = img.crop((left, top, left + 240, top + 240))
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_b1", "efficientnet_b1.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{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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