---
license: apache-2.0
---

# Shot Scale

This model predicts an image's cinematic camera angle [extreme_close_up, close_up, medium, full, wide].  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/shot_scale')
model.eval()

# example medium shot image
# Model labels: [extreme_close_up, close_up, medium, full, wide]
image = Image.open('medium.jpg')
inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

# technically multi-label training, but argmax works too!
predicted_label = outputs.logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```

## Performance:

Due to very low representation for ECU, the performance on that category is less than desirable.  In the next version we will oversample ECU images.  Also note that Wide and Full shots overlap quite a bit.  In practice, a full shot is often a wide shot with a human subject.

| Category | Precision | Recall |
|----------|-----------|--------|
| ECU (low coverage)      | 75%        | 32%     |
| CU       | 66%       | 51%    |
| M        | 88%       | 90%    |
| F        | 69%       | 68%    |
| W        | 89%       | 83%    |