---
library_name: transformers
tags:
- shot type
- shot scale
- movienet
- movieshots
- video classification
license: mit
metrics:
- accuracy
- f1
pipeline_tag: video-classification
---

# VideoMAE finetuned for shot scale classification

**videomae-base-finetuned-kinetics** model finetuned to classify shot scale into five classes: *ECS (Extreme close-up shot), CS (close-up shot), MS (medium shot), FS (full shot), LS (long shot)*

[Movienet](https://movienet.github.io/projects/eccv20shot.html) dataset is used for finetuning the model for 5 epochs. *v1_split_trailer.json* provides the training, validation and test data splits.


## Evaluation

Model achieves accuracy of 88.93% and macro-f1 of 89.19%

Class-wise accuracies: ECS - 91.16%, CS - 83.65, MS - 86.2%, FS - 90.74%, LS - 94.55%


## How to use

This is how model can be tested on a shot/clip from a video. 
Same code is used to process, transform and evaluate on the movienet test set.

```python
from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification
from pytorchvideo.transforms import ApplyTransformToKey
from torchvision.transforms import v2
from decord import VideoReader, cpu

## Evaluation Transform
transform = v2.Compose(
    [
        ApplyTransformToKey(
            key="video",
            transform=v2.Compose(
                [
                    v2.Lambda(lambda x: x.permute(0, 3, 1, 2)), # T, H, W, C -> T, C, H, W
                    v2.UniformTemporalSubsample(16),
                    v2.Resize(resize_to),
                    v2.Lambda(lambda x: x / 255.0),
                    v2.Normalize(img_mean, img_std)
                ]
            ),
        ),
    ]
)

## Preprocessor and Model loading
image_processor = VideoMAEImageProcessor.from_pretrained("gullalc/videomae-base-finetuned-kinetics-movieshots-scale")
model = VideoMAEForVideoClassification.from_pretrained("gullalc/videomae-base-finetuned-kinetics-movieshots-scale")

img_mean = image_processor.image_mean
img_std = image_processor.image_std
height = width = image_processor.size["shortest_edge"]
resize_to = (height, width)

## load video/clip and predict
video_path = "random_clip.mp4"
vr = VideoReader(video_path, width=480, height=270, ctx=cpu(0))
frames_tensor = torch.stack([torch.tensor(vr[i].asnumpy()) for i in range(len(vr))])  ## Shape: (T, H, W, C)

frames_tensor = transform({"video": frames_tensor})["video"]

output = model(pixel_values=frames_tensor)
pred = torch.argmax(outputs.logits, axis=1).cpu().numpy()

print(model.config.id2label[pred[0]])
```