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Microsoft Resnet 152 Plant Seedling Classification
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Microsoft Resnet 152 Plant Seedling Classification

Developed by uisikdag
Plant seedling classification model fine-tuned based on ResNet-152, achieving 77.67% accuracy on the test set
Image Classification
Transformers
Open Source License:Apache-2.0#Plant seedling classification#Agricultural image recognition#ResNet fine-tuning
Downloads 30
Release Time : 3/9/2023

Model Overview

This model is used to identify different types of plant seedlings, especially suitable for agricultural weed recognition scenarios. Trained on a balanced dataset with 250 samples per class, image resolution is 224x224.

Model Features

Balanced dataset training
Trained using a balanced dataset with 250 samples per class to avoid class imbalance issues
High-resolution processing
Input images are uniformly adjusted to 224x224 resolution to retain more detail information
Transfer learning optimization
Fine-tuned based on microsoft/resnet-152 pre-trained model to improve training efficiency

Model Capabilities

Plant seedling classification
Agricultural weed recognition
Image feature extraction

Use Cases

Agricultural technology
Automatic weed recognition
Automatically identify weed species in farmland monitoring systems
Test set accuracy 77.67%
Crop seedling classification
Classify and identify different crop seedlings at various growth stages
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