---
license: cc-by-4.0
metrics:
- accuracy
---

# Image Scenery Classification
This model is built on the efficientnet_b2 architecture.
The model uses pretrained weights of the model found in the torchvision.models library.
The classification head was changed, to be a dropout layer, followed by a linear layer with 6 target classes.  
Using transfer learning, the model was then trained on the [intel image dataset.](https://www.kaggle.com/datasets/puneet6060/intel-image-classification)  
See the corresponding [hugging face space](https://huggingface.co/spaces/richardschattner/scenery_classification) for a live demo of the model.

#### Performance
The model achieved a test accuracy of 89,67%.  
Misclassified images are often times ambiguous, such as a snowy mountain, being misclassified as 'glacier'.  
The model architecture is quite simple, when compared to SOTA architectures and produces fast predictions.
A prediction on the hugging face space, hosted on the free cpu, takes about 0.2 seconds.

The code is original and written by me.