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License: MIT License
Fire and smoke detection using spatial and temporal patterns.
License: MIT License
I am running the model with python3 baseline.py --video ex1.mp4 --model mobilenet
, but it only shows some bash outputs and no output videos. Does it save the output file somewhere?
1/1 [==============================] - 0s 25ms/step
Time taken = 0.04462027549743652
Prediction: non_fire
1/1 [==============================] - 0s 25ms/step
Time taken = 0.043842315673828125
Prediction: non_fire
1/1 [==============================] - 0s 26ms/step
Time taken = 0.04700660705566406
Prediction: non_fire
1/1 [==============================] - 0s 27ms/step
Time taken = 0.050565481185913086
Prediction: non_fire
Results
video network detected first_frame time_avg
0 ex1.mp4 mobilenet True 10 0.049584
Thank you for your work "A hybrid method for fire detection based on spatial and temporal patterns (2023)".
My question is:
About test video: Is a video start with "FP" is the positive video (having fire/smoke) and those starting with "VP" is the negative sample?
The video:
About the result table 3: are you use these following values as provided in the code (or the different ones) to produce this result table?
+ conf_thres=0.25, # confidence threshold
+ iou_thres=0.45, # NMS IOU threshold"
Thank you for your clarification!
I was trying your baseline models (Firenet and Mobilenet). Are they for Fire only and not Smoke?
python baseline.py --video <video_file> --model firenet
Hi, I want to ask how can I convert this code into yolov8 format to use with temporal features after detection? Please tell me how can I do this?
I have a hard time running it. I guess because the code is for a few years ago and I get issues with new API & A100 GPU etc.
Is there any plans to test or address issues with newer library versions, new GPUs, etc?
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
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