File size: 1,852 Bytes
f742f90
7d7fc09
 
 
 
f742f90
 
 
175f77d
f742f90
175f77d
f742f90
4f1f45e
7d7fc09
5fdda9d
 
7d7fc09
5fdda9d
7d7fc09
 
f742f90
 
 
4f1f45e
 
5fdda9d
 
1666a2c
 
ba41881
f742f90
1666a2c
 
adb3770
96abed4
ce5dd53
 
 
16b6351
ba41881
 
 
 
 
ce5dd53
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
---
size_categories:
- 10K<n<100K
task_categories:
- image-to-image
dataset_info:
  features:
  - name: lensless
    dtype: image
  - name: lensed
    dtype: image
  splits:
  - name: train
    num_bytes: 5600452730.0
    num_examples: 24000
  - name: test
    num_bytes: 230987060.0
    num_examples: 999
  download_size: 5873531182
  dataset_size: 5831439790.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
tags:
- lensless
- computational-imaging
---


More accessible (6GB instead of 100GB) copy of: https://waller-lab.github.io/LenslessLearning/dataset.html

Original license: https://github.com/Waller-Lab/LenslessLearning/blob/master/LICENSE

This dataset was prepared with [this script](https://github.com/LCAV/LenslessPiCam/blob/main/scripts/data/upload_diffusercam_huggingface.py).

After cloning and installing [LenslessPiCam](https://github.com/LCAV/LenslessPiCam), ADMM reconstruction can be applied to the dataset with [this script](https://github.com/LCAV/LenslessPiCam/blob/main/scripts/recon/dataset.py) (handles dataset downloading from Hugging Face).
```bash
python scripts/recon/dataset.py
```

The models in [this collection](https://huggingface.co/collections/bezzam/diffusercam-mirflickr-65c05164df72cf99e5066658) use the [original DiffuserCam MirFlickr dataset](https://waller-lab.github.io/LenslessLearning/dataset.html) during training.
This dataset tries to replicate that version of the dataset (using NPY files during training).
One slight different is that we were required to subtract the mininum of value the numpy arrays so that they could be stored as viewable images.
For future training, it is recommended to use [this normalized version](https://huggingface.co/datasets/bezzam/DiffuserCam-Lensless-Mirflickr-Dataset-NORM) of the dataset.