videomae-base-finetuned-ucfcrime-full2

This model is a fine-tuned version of MCG-NJU/videomae-base on the UCF-CRIME dataset. code : github It achieves the following results on the evaluation set:

• Loss: 2.5014
• Accuracy: 0.225

Model description

More information needed

Intended uses & limitations

Inference using phone camera (have to download ipwebcam on phone from playstore)

import cv2
import torch
import numpy as np
from transformers import AutoImageProcessor, VideoMAEForVideoClassification

np.random.seed(0)

def preprocess_frames(frames, image_processor):
    inputs = image_processor(frames, return_tensors="pt")
    inputs = {k: v.to(device) for k, v in inputs.items()}  # Move tensors to GPU
    return inputs

# Initialize the video capture object, replace ip addr with the local ip of your phone  (will be shown in the ipwebcam app)
cap = cv2.VideoCapture('http://192.168.229.98:8080/video')

# Set the frame size (optional)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

image_processor = AutoImageProcessor.from_pretrained("archit11/videomae-base-finetuned-ucfcrime-full")
model = VideoMAEForVideoClassification.from_pretrained("archit11/videomae-base-finetuned-ucfcrime-full")

# Move the model to GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

frame_buffer = []
buffer_size = 16
previous_labels = []
top_confidences = []  # Initialize top_confidences

while True:
    ret, frame = cap.read()

    if not ret:
        print("Failed to capture frame")
        break

    # Add the current frame to the buffer
    frame_buffer.append(frame)

    # Check if we have enough frames for inference
    if len(frame_buffer) >= buffer_size:
        # Preprocess the frames
        inputs = preprocess_frames(frame_buffer, image_processor)

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

        # Get the top 3 predicted labels and their confidence scores
        top_k = 3
        probs = torch.softmax(logits, dim=-1)
        top_probs, top_indices = torch.topk(probs, top_k)
        top_labels = [model.config.id2label[idx.item()] for idx in top_indices[0]]
        top_confidences = top_probs[0].tolist()  # Update top_confidences

        # Check if the predicted labels are different from the previous labels
        if top_labels != previous_labels:
            previous_labels = top_labels
            print("Predicted class:", top_labels[0])  # Print the predicted class for debugging

        # Clear the frame buffer and continue from the next frame
        frame_buffer.clear()

        # Display the predicted labels and confidence scores on the frame
        for i, (label, confidence) in enumerate(zip(previous_labels, top_confidences)):
            label_text = f"{label}: {confidence:.2f}"
            cv2.putText(frame, label_text, (10, 30 + i * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

        # Display the resulting frame
        cv2.imshow('Video', frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# Release everything when done
cap.release()
cv2.destroyAllWindows() 

Simple usage

Usage:

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"
)
# use any other video just replace `file_path` with the video path
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("archit11/videomae-base-finetuned-ucfcrime-full")
model = VideoMAEForVideoClassification.from_pretrained("archit11/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])

Inference Using

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

Framework versions

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