Accuracy: 99.26
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Fine-Grained Image Classification
Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Looking for the Devil in the Details )</span>
논문
Top-1 Error Rate: 2.68%Accuracy: 97.32
Accuracy (%): 79.90
Top-1 Error Rate: 4.06%
Top-1 Error Rate: 7%
Accuracy: 88.1%
Accuracy: 85.1%
Accuracy: 88.3%
Accuracy: 87.7%
Top-1 Error Rate: 4.42%Accuracy: 95.58
Accuracy: 91.6
Accuracy: 88.2%
Accuracy: 91.6%
Accuracy: 76.4%
Accuracy: 88.59
Accuracy: 95.00
Accuracy: 95.86
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
Accuracy: 91.1%
Top-1 Error Rate: 1.98%
Top-1 Error Rate: 6.5%Accuracy: 93.5%
Accuracy: 91.7
Accuracy: 91.3%
Top-1 Error Rate: 9.798%
Top-1 Error Rate: 9.0%Accuracy: 91.0
Top-1 Error Rate: 8.7%