Understanding Optimal T, H, and W for R3D_18 Pretrained on Kinetics-400

Hi everyone,

I’m working on a 3D CNN for defect detection. My dataset is such that a single data is a 3D volume (512×1024×1024), but due to computational constraints, I plan to use a sliding window approach** with 16×16×16 voxel chunks as input to the model. I have a corresponding label for each voxel chunk.

I plan to use R3D_18 (ResNet-3D 18) with Kinetics-400 pre-trained weights, but I’m unsure about the settings for the temporal (T) and spatial (H, W) dimensions.

Questions:

1. How should I handle grayscale images with this RGB pre-trained model? Should I modify the first layer from C = 3 to C = 1? I’m not sure if this would break the pre-trained weights and not lead to effective training
2. Should the T, H, and W values match how the model was pre-trained, or will it cause issues if I use different dimensions based on my data? For me, T = 16, H = 16, and W = 16, and I need it this way (or 32 × 32 × 32), but I want to clarify if this would break the pre-trained weights and prevent effective training.

Any insights would be greatly appreciated! Thanks in advance.