Depending on what you want the "feature" to represent, choose a model:
This results in a vector (e.g., size 2048 for ResNet-50).
Since a video is a sequence of images, you first need to sample frames. For a 5.75 MB file (likely a short clip), sampling or taking a fixed number (e.g., 16 frames) is standard. 2. Select a Pre-trained Model Download: video5179512026745012956.mp4 (5.75 MB)
Use ResNet-50 or ViT (Vision Transformer) pre-trained on ImageNet.
Instead of the final classification layer (which would say "dog" or "running"), you extract the output from the (often called the "bottleneck" or "pooling layer"). Depending on what you want the "feature" to
Use a 3D CNN like I3D or VideoMAE which processes temporal data. 3. Pre-process the Data
import torch import torchvision.models as models import torchvision.transforms as T from PIL import Image import cv2 # 1. Load pre-trained ResNet model = models.resnet50(pretrained=True) model = torch.nn.Sequential(*(list(model.children())[:-1])) # Remove last layer model.eval() # 2. Define Transform preprocess = T.Compose([ T.Resize(256), T.CenterCrop(224), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) # 3. Process a frame from video5179512026745012956.mp4 cap = cv2.VideoCapture('video5179512026745012956.mp4') ret, frame = cap.read() if ret: img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) input_tensor = preprocess(img).unsqueeze(0) with torch.no_grad(): deep_feature = model(input_tensor) # This is your feature vector Use code with caution. Copied to clipboard AI responses may include mistakes. Learn more Use a 3D CNN like I3D or VideoMAE
To prepare a "deep feature" (a high-dimensional vector representation) for the video file video5179512026745012956.mp4 , you will typically follow a computer vision pipeline using a pre-trained deep learning model. 1. Extract Representative Frames
