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

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

archit11
/

videomae-base-finetuned-ucfcrime-full