# Model card for timm-mobilevitv2_050-beans

This model is a fine-tuned version of `mobilevitv2_050` (from timm) on the `beans` dataset. It achieves the following results on the validation set:

- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846

## Image normalization

Imagenet

```python
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
```
{"tags": ["image-classification", "timm"], "datasets": ["beans"], "library_tag": "timm", "widget": [{"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg", "example_title": "Healthy"}, {"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg", "example_title": "Angular Leaf Spot"}, {"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/main/bean_rust.jpeg", "example_title": "Bean Rust"}]}
timm pytorch image-classification dataset:beans region:us
TAGS
#timm #pytorch #image-classification #dataset-beans #region-us 

# Model card for timm-mobilevitv2_050-beans

This model is a fine-tuned version of 'mobilevitv2_050' (from timm) on the 'beans' dataset. It achieves the following results on the validation set:

- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846

## Image normalization

Imagenet


# Model card for timm-mobilevitv2_050-beans

This model is a fine-tuned version of 'mobilevitv2_050' (from timm) on the 'beans' dataset. It achieves the following results on the validation set:

- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846 ## Image normalization

Imagenet
TAGS
#timm #pytorch #image-classification #dataset-beans #region-us 
 # Model card for timm-mobilevitv2_050-beans

This model is a fine-tuned version of 'mobilevitv2_050' (from timm) on the 'beans' dataset. It achieves the following results on the validation set:

- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846 ## Image normalization

Imagenet
passage: TAGS
#timm #pytorch #image-classification #dataset-beans #region-us 
# Model card for timm-mobilevitv2_050-beans

This model is a fine-tuned version of 'mobilevitv2_050' (from timm) on the 'beans' dataset. It achieves the following results on the validation set:

- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846## Image normalization

Imagenet