This results in a vector (e.g., size 2048 for ResNet-50).
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)
Convert the images into numerical arrays (tensors). 4. Extract the Global Feature Vector This results in a vector (e
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 16 frames) is standard.
Use a 3D CNN like I3D or VideoMAE which processes temporal data. 3. Pre-process the Data
Depending on what you want the "feature" to represent, choose a model:
Use ResNet-50 or ViT (Vision Transformer) pre-trained on ImageNet.