RapidGrad is a research paper and a method proposed by researchers at Google, published in 2016. The paper presents a novel optimization algorithm for deep learning, called Rapid Gradient Sign Descent (RapidGrad).
Built to work within DaVinci Wide Gamut , making it compatible with various camera types and future-proof for high dynamic range (HDR) projects. Benefits for Users rapidgrad
def predict(image): # your model inference return processed_image RapidGrad is a research paper and a method
The plugin streamlines complex grading workflows into a more intuitive interface: rapidgrad
# Feature: Gradient accumulation for large batches scaler = torch.cuda.amp.GradScaler() for i, (inputs, labels) in enumerate(dataloader): with torch.autocast(device_type='cuda'): loss = model(inputs, labels) scaler.scale(loss).backward() if (i+1) % accumulation_steps == 0: scaler.step(optimizer) scaler.update() optimizer.zero_grad()