|
|
--- |
|
|
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. |
