---
license: mit
library_name: transformers
tags:
- camera level
- camera feature
- movie analysis
metrics:
- accuracy
- f1
pipeline_tag: image-classification
---

# Convnextv2 finetuned for camera level classification 

Convnextv2 base-size model finetuned for the classification of camera angles. [Cinescale](https://cinescale.github.io/camera_al/#dataset) dataset is used to finetune the model for 20 epochs.

Classifies an image into six classes: *aerial, eye, ground, hip, knee, shoulder*


## Evaluation

On the test set (test.csv), the model has an accuracy of 89.82% and macro-f1 of 82.31%


## How to use

```python
from transformers import AutoModelForImageClassification
import torch
from torchvision.transforms import v2
from torchvision.io import read_image, ImageReadMode

model = AutoModelForImageClassification.from_pretrained("gullalc/convnextv2-base-22k-384-cinescale-level")
im_size = 384

# https://www.pexels.com/photo/aerial-view-of-city-buildings-8783146/
image = read_image("demo/level_demo.jpg", mode=ImageReadMode.RGB)

transform = v2.Compose([v2.Resize((im_size,im_size), antialias=True),
                            v2.ToDtype(torch.float32, scale=True),
                            v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

inputs = transform(image).unsqueeze(0)

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

predicted_label = model.config.id2label[torch.argmax(outputs.logits).item()]
print(predicted_label)
# --> aerial
```