---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
- vandalism
- video-classification
- ucf-crime
- vandalism-dectection
- videomae
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucfcrime-full2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# videomae-base-finetuned-ucfcrime-full2

This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on the [UCF-CRIME](https://paperswithcode.com/dataset/ucf-crime)
dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5014
- Accuracy: 0.225

## Model description

More information needed

## Intended uses & limitations

Usage: 
```python
import av
import torch
import numpy as np

from transformers import AutoImageProcessor, VideoMAEForVideoClassification
from huggingface_hub import hf_hub_download

np.random.seed(0)


def read_video_pyav(container, indices):
    '''
    Decode the video with PyAV decoder.
    Args:
        container (`av.container.input.InputContainer`): PyAV container.
        indices (`List[int]`): List of frame indices to decode.
    Returns:
        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
    '''
    frames = []
    container.seek(0)
    start_index = indices[0]
    end_index = indices[-1]
    for i, frame in enumerate(container.decode(video=0)):
        if i > end_index:
            break
        if i >= start_index and i in indices:
            frames.append(frame)
    return np.stack([x.to_ndarray(format="rgb24") for x in frames])


def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
    '''
    Sample a given number of frame indices from the video.
    Args:
        clip_len (`int`): Total number of frames to sample.
        frame_sample_rate (`int`): Sample every n-th frame.
        seg_len (`int`): Maximum allowed index of sample's last frame.
    Returns:
        indices (`List[int]`): List of sampled frame indices
    '''
    converted_len = int(clip_len * frame_sample_rate)
    end_idx = np.random.randint(converted_len, seg_len)
    start_idx = end_idx - converted_len
    indices = np.linspace(start_idx, end_idx, num=clip_len)
    indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
    return indices


# video clip consists of 300 frames (10 seconds at 30 FPS)
file_path = hf_hub_download(
    repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
)
container = av.open(file_path)

# sample 16 frames
indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames)
video = read_video_pyav(container, indices)

image_processor = AutoImageProcessor.from_pretrained("videomae-base-finetuned-ucfcrime-full")
model = VideoMAEForVideoClassification.from_pretrained("videomae-base-finetuned-ucfcrime-full")

inputs = image_processor(list(video), return_tensors="pt")

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

# model predicts one of the 13 ucf-crime classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```
## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 700

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5836        | 0.13  | 88   | 2.4944          | 0.2080   |
| 2.3212        | 1.13  | 176  | 2.5855          | 0.1773   |
| 2.2333        | 2.13  | 264  | 2.6270          | 0.1046   |
| 1.985         | 3.13  | 352  | 2.4058          | 0.2109   |
| 2.194         | 4.13  | 440  | 2.3654          | 0.2235   |
| 1.9796        | 5.13  | 528  | 2.2609          | 0.2235   |
| 1.8786        | 6.13  | 616  | 2.2725          | 0.2341   |
| 1.71          | 7.12  | 700  | 2.2228          | 0.2226   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2