couldn't get LLFF scene grade

[that-liu]

I used python train.py -scene ${PATH_TO_SCENE} -model_dir ${MODEL_TO_SAVE_CHECKPOINT} -http-cv2resize in the flower scene of llff, But did not get the grade of the paper, using four rtx2080Ti,
Measurement Result
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
name PSNR SSIM LPIPS
images_IMG_2962.JPG 25.110090 0.873111 0.24526712
images_IMG_2970.JPG 26.380335 0.898152 0.20776317
images_IMG_2978.JPG 25.021733 0.861492 0.22302648
images_IMG_2986.JPG 28.067660 0.920080 0.17454995
images_IMG_2994.JPG 28.821845 0.926312 0.17679015

PSNR 26.680332
SSIM 0.895829
LPIPS 0.205479

[pureexe]

Last month, someone emailed me about this problem. I investigated this and confirmed that our code has a different score than the score reported in the paper.

In case you want to use NeX as a baseline comparison. In that case, you can report the score from either retraining, measuring directly from the provided result in the dataset directory, or taking directly from the paper, which is fine for me.

Here is the table showing how the score is drifting on Crest and Trex

Crest

Crest	PSNR	SSIM	LPIPS
Paper report	21.23	0.757	0.162
Retrained	20.72	0.714	0.177

Trex

Trex	PSNR	SSIM	LPIPS
Paper report	28.73	0.953	0.192
Retrained	28.69	0.952	0.192