Plant Disease Detection Project

🚀 MobileNet V2

A pre - trained MobileNet V2 model on ImageNet - 1k at 224x224 resolution, offering efficient image classification solutions.

🚀 Quick Start

You can use the raw model for image classification. See the model hub to look for fine - tuned versions on a task that interests you.

✨ Features

• Lightweight Design: MobileNets are small, low - latency, low - power models parameterized to meet the resource constraints of a variety of use cases.
• Versatile Applications: They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large - scale models, such as Inception, are used.
• Efficient on Mobile: MobileNets can be run efficiently on mobile devices.
• Trade - off Optimization: MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

preprocessor = AutoImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224")
model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224")

inputs = preprocessor(images=image, return_tensors="pt")

outputs = model(**inputs)
logits = outputs.logits

# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0). Currently, both the feature extractor and model support PyTorch.

📚 Documentation

Model description

From the original README:

MobileNets are small, low - latency, low - power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large - scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

The checkpoints are named mobilenet_v2_depth_size, for example mobilenet_v2_1.0_224, where 1.0 is the depth multiplier and 224 is the resolution of the input images the model was trained on.

BibTeX entry and citation info

@inproceedings{mobilenetv22018,
  title={MobileNetV2: Inverted Residuals and Linear Bottlenecks},
  author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang - Chieh Chen},
  booktitle={CVPR},
  year={2018}
}

📄 License

License: other

Information Table