File size: 4,706 Bytes
45c1003 36bae08 8e450b7 45c1003 8e450b7 45c1003 8e450b7 e4f93a0 8e450b7 e4f93a0 8e450b7 e4f93a0 8e450b7 e4f93a0 45c1003 8e450b7 45c1003 8e450b7 21533ca 8e450b7 21533ca 8e450b7 21533ca | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | ---
library_name: litert
pipeline_tag: image-classification
tags:
- vision
- image-classification
- google
- computer-vision
datasets:
- imagenet-1k
model-index:
- name: litert-community/efficientnet_v2_m
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: 0.8512
- name: Top 5 Accuracy (Full Precision)
type: accuracy
value: 0.9716
- name: Top 1 Accuracy (Dynamic Quantized wi8 afp32)
type: accuracy
value: 0.7208
- name: Top 5 Accuracy (Dynamic Quantized wi8 afp32)
type: accuracy
value: 0.8613
---
# EfficientNet V2 M
The EfficientNetV2-M is a high-capacity model pre-trained on ImageNet-1k, originally introduced by Tan, Mingxing, Le and Quoc in the 2021 paper, [**EfficientNetV2: Smaller Models and Faster Training**](https://arxiv.org/abs/2104.00298). This architecture evolves the original compound scaling formula by incorporating Fused-MBConv layers and progressive learning—a method that dynamically adjusts image resolution and regularization during training.
## Model description
The model was converted from a checkpoint from PyTorch Vision.
The original model has:
acc@1 (on ImageNet-1K): 85.112%
acc@5 (on ImageNet-1K): 97.156%
num_params: 54,139,356
## 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 = 480
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] - 480) // 2
top = (img.size[1] - 480) // 2
img = img.crop((left, top, left + 480, top + 480))
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_v2_m", "efficientnet_v2_m.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
@inproceedings{tan2021efficientnetv2,
title={Efficientnetv2: Smaller models and faster training},
author={Tan, Mingxing and Le, Quoc},
booktitle={International conference on machine learning},
pages={10096--10106},
year={2021},
organization={PMLR}
}
```
|