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您好,我下载pretrain-model后,使用以下命令,跑10s视频的inference python3.7 -B -m paddle.distributed.launch --gpus='0' --log_dir=$LOG_DIR main.py --test -c data/soccernet_inference/soccernet_pptimesformer_k400_one_file_inference.yaml -w $WEIGHT_FILE -o inference_dir=$INFERENCE_DIR -o DATASET.test.file_path=$VIDEO_CONFIG
python3.7 -B -m paddle.distributed.launch --gpus='0' --log_dir=$LOG_DIR main.py --test -c data/soccernet_inference/soccernet_pptimesformer_k400_one_file_inference.yaml -w $WEIGHT_FILE -o inference_dir=$INFERENCE_DIR -o DATASET.test.file_path=$VIDEO_CONFIG
在结果文件中, cls_score_all.shape: [5, 1, 18], event_times.shape: [5, 1, 18], 请问下这里18维对应的label顺序是什么,以及event_times代表什么含义?
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cls_score_all 和 event_times暂时不用管,当时是为了end 2 end训练预留的,但是后来没来的及实现,所以现在暂时没什么用,feature key下面的数才有用。
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Hello @paulxinzhou , What these 'features' data mean? Sorry for asking but really getting a hard time to understand the inference result.
@realsazzad I added some explanation here. https://github.com/baidu-research/Soccernet-features/blob/develop/README.md#understanding-the-result-features
Let me know if there's still confusion.
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您好,我下载pretrain-model后,使用以下命令,跑10s视频的inference
python3.7 -B -m paddle.distributed.launch --gpus='0' --log_dir=$LOG_DIR main.py --test -c data/soccernet_inference/soccernet_pptimesformer_k400_one_file_inference.yaml -w $WEIGHT_FILE -o inference_dir=$INFERENCE_DIR -o DATASET.test.file_path=$VIDEO_CONFIG
在结果文件中, cls_score_all.shape: [5, 1, 18], event_times.shape: [5, 1, 18], 请问下这里18维对应的label顺序是什么,以及event_times代表什么含义?
The text was updated successfully, but these errors were encountered: