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Explanation for scene boundary prediction #5
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Simply, apply softmax to generate probaility and then thresholding the value at the second dimension by 0.5 provides the binary prediction result. For example,
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Thank you. cfg = init_hydra_config(mode="extract_shot")
apply_random_seed(cfg)
cfg = load_pretrained_config(cfg)
# init model
cfg, model = init_model(cfg)
# init trainer
cfg, trainer = init_trainer(cfg) Is this right? |
For loading a BaSSL 40 epochs scene segmentation model in
Then, you also need to specify For an input, our algorithm works on top of |
Hello, I would also be interested in knowing more details on how to run the code for inference starting from a fine-tuned model, I tried using @JonghwanMun but couldn't come up with working code. Is it correct to init the cfg this way, and would "finetune" be the correct mode here?
About the data, I have two questions:
Thank you |
Hello, I see FinetuningWrapper.load_from_checkpoint in main_utils.py, but i cannot find the implementation of load_from_checkpoint in finetune_wrapper.py, I wonder how it works, thanks |
@barry2025 |
Thanks! I never used pytorch lightning before, I'll try. |
Hello,
I read that you might be working on a demo on how to predict on a single video.
I was able to create my own dataloader and call trainer.predict() but the output is not binary (boundary or not boundary).
Does this model support scene boundary prediction (if so could you detail what are the steps? I just need to understand how i can make it work) or is it only a shot encoding model?
Thank you very much
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