(5.75 Mb) | Download: Video5179512026745012956.mp4

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

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"). Download: video5179512026745012956.mp4 (5.75 MB)

Subtract the mean and divide by the standard deviation (specific to the dataset the model was trained on). import torch import torchvision

Depending on what you want the "feature" to represent, choose a model: Process a frame from video5179512026745012956

This results in a vector (e.g., size 2048 for ResNet-50).

Use a 3D CNN like I3D or VideoMAE which processes temporal data. 3. Pre-process the Data