README.md

---
license: apache-2.0
---

# Deoth of Field

This model predicts an image's cinematic depth of field [deep, shallow].  The model is a DinoV2 with registers backbone (initiated with `facebook/dinov2-with-registers-large` weights) and trained on a diverse set of five thousand human-annotated images.

## How to use:
```python

import torch
from PIL import Image
from transformers import AutoImageProcessor
from transformers import AutoModelForImageClassification

image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-with-registers-large")
model = AutoModelForImageClassification.from_pretrained('aslakey/depth_of_field')
model.eval()

# Model labels: [deep, shallow]
image = Image.open('cinematic_shot.jpg')
inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

predicted_label = outputs.logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```

## Performance:


| Category | Precision | Recall |
|----------|-----------|--------|
| deep      | 85%        | 77%     |
| shallow        | 75%       | 84%    |