Ajibola
/

PaViT

@@ -14,7 +14,7 @@ pipeline_tag: image-classification
 <h1>Model Performance</h1>
 PaViT was trained on a 4GB RAM CPU using a dataset of 15000 Kaggle images of 15 classes, achieving a remarkable 88% accuracy with 4 self-attention heads. The model's accuracy further improved to 96% when trained with 12 self-attention heads and 12 linearly stacked linear layers. These results demonstrate the model's impressive performance and fast training speed on a CPU, despite being trained on a relatively small dataset.
-The uploaded weight was trained on animage dataset of 3 classes (Cat, Dog and Wild animal)
 <h1>Usage</h1>
 The model can be used for image recognition tasks by using the trained weights provided in the repository. The code can be modified to use custom datasets, and the model's performance can be further improved by adding more self-attention heads and linear layers.

 <h1>Model Performance</h1>
 PaViT was trained on a 4GB RAM CPU using a dataset of 15000 Kaggle images of 15 classes, achieving a remarkable 88% accuracy with 4 self-attention heads. The model's accuracy further improved to 96% when trained with 12 self-attention heads and 12 linearly stacked linear layers. These results demonstrate the model's impressive performance and fast training speed on a CPU, despite being trained on a relatively small dataset.
+<br>The uploaded weight was trained on image dataset of 3 classes (Cat, Dog and Wild animal) </br>
 <h1>Usage</h1>
 The model can be used for image recognition tasks by using the trained weights provided in the repository. The code can be modified to use custom datasets, and the model's performance can be further improved by adding more self-attention heads and linear layers.