
# Model Trained Using AutoTrain

- Problem type: Image Classification

<!-- ## Validation Metrics
loss: 0.5462027192115784

f1_macro: 0.38996247906197656

f1_micro: 0.737093690248566

f1_weighted: 0.6627689294144399

precision_macro: 0.3467645553924699

precision_micro: 0.737093690248566

precision_weighted: 0.6320379754980795

recall_macro: 0.49719101123595505

recall_micro: 0.737093690248566

recall_weighted: 0.737093690248566

accuracy: 0.737093690248566 -->

# Image Classification Model Results (AutoTrain)

## Validation Metrics

| Metric | Value |
|--------|-------|
| Loss | 0.5462 |
| Accuracy | 0.7371 |

### F1 Scores
| Type | Value |
|------|-------|
| Macro | 0.3900 |
| Micro | 0.7371 |
| Weighted | 0.6628 |

### Precision
| Type | Value |
|------|-------|
| Macro | 0.3468 |
| Micro | 0.7371 |
| Weighted | 0.6320 |

### Recall
| Type | Value |
|------|-------|
| Macro | 0.4972 |
| Micro | 0.7371 |
| Weighted | 0.7371 |


## How to use

This model is designed for image classification. Here's how you can use it:

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

model_name = "eligapris/v-mdd-2000"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

image = Image.open("path_to_your_image.jpg")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
common_rust gray_leaf_spot healthy_leaf northern_leaf_blight