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tags = {} for f in frames: img = Image.open(f) input_t = preprocess(img).unsqueeze(0) with torch.no_grad(): out = model(input_t) prob, idx = torch.topk(out.softmax(dim=1), k=3) for p, i in zip(prob[0], idx[0]): tag = classes[i] tags[tag] = max(tags.get(tag, 0), float(p)) # Convert to list of dicts sorted by confidence return ["tag": t, "confidence": round(c, 3) for t, c in sorted(tags.items(), key=lambda x: -x[1])[:5]] There's often a stigma attached to victims of
