Update docs for `nyu_depth_v2` dataset
State: closed | Author: awsaf49 (CONTRIBUTOR)

This PR will fix the issue mentioned in #5461. Here is brief overview,

## Bug:

Discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-side comparison,

![image](https://user-images.githubusercontent.com/36858976/214381162-1d9582c2-6750-4114-a01a-61ca1cd5f872.png)


## Fix:

When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py`  came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32`  hence the **discretized** depth map.
https://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93


cc: @sayakpaul @lhoestq


Comments: I think I need to create another PR on https://huggingface.co/datasets/huggingface/documentation-images/tree/main/datasets for hosting the images there?

_The documentation is not available anymore as the PR was closed or merged._

Thanks for the update @awsaf49 !

> Thanks a lot for the updates!
> 
> Just some minor things remain and the we should be good to ship this 🚀

@sayakpaul I have updated the minor things. Please approve the workflows

I think this PR is good to go..
@sayakpaul @lhoestq 

<details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.009064 / 0.011353 (-0.002289) | 0.005262 / 0.011008 (-0.005746) | 0.099608 / 0.038508 (0.061100) | 0.035015 / 0.023109 (0.011906) | 0.296501 / 0.275898 (0.020602) | 0.353619 / 0.323480 (0.030139) | 0.007903 / 0.007986 (-0.000083) | 0.004093 / 0.004328 (-0.000235) | 0.075260 / 0.004250 (0.071009) | 0.043142 / 0.037052 (0.006089) | 0.307755 / 0.258489 (0.049266) | 0.336340 / 0.293841 (0.042499) | 0.038596 / 0.128546 (-0.089950) | 0.011861 / 0.075646 (-0.063786) | 0.334226 / 0.419271 (-0.085045) | 0.051472 / 0.043533 (0.007940) | 0.298539 / 0.255139 (0.043400) | 0.316856 / 0.283200 (0.033656) | 0.108620 / 0.141683 (-0.033063) | 1.434901 / 1.452155 (-0.017254) | 1.468368 / 1.492716 (-0.024348) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.208402 / 0.018006 (0.190395) | 0.445799 / 0.000490 (0.445309) | 0.003704 / 0.000200 (0.003504) | 0.000084 / 0.000054 (0.000030) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025435 / 0.037411 (-0.011976) | 0.105874 / 0.014526 (0.091348) | 0.115652 / 0.176557 (-0.060905) | 0.150872 / 0.737135 (-0.586263) | 0.121705 / 0.296338 (-0.174633) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.397816 / 0.215209 (0.182607) | 3.977766 / 2.077655 (1.900111) | 1.850848 / 1.504120 (0.346728) | 1.686062 / 1.541195 (0.144867) | 1.786277 / 1.468490 (0.317787) | 0.696250 / 4.584777 (-3.888527) | 3.785255 / 3.745712 (0.039543) | 3.355013 / 5.269862 (-1.914849) | 1.818232 / 4.565676 (-2.747444) | 0.085408 / 0.424275 (-0.338867) | 0.012567 / 0.007607 (0.004960) | 0.524185 / 0.226044 (0.298140) | 5.061975 / 2.268929 (2.793047) | 2.299866 / 55.444624 (-53.144758) | 1.966709 / 6.876477 (-4.909768) | 2.018760 / 2.142072 (-0.123313) | 0.841341 / 4.805227 (-3.963886) | 0.166374 / 6.500664 (-6.334290) | 0.061854 / 0.075469 (-0.013615) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.221666 / 1.841788 (-0.620122) | 14.373194 / 8.074308 (6.298886) | 14.253614 / 10.191392 (4.062222) | 0.172979 / 0.680424 (-0.507445) | 0.029176 / 0.534201 (-0.505025) | 0.447399 / 0.579283 (-0.131884) | 0.443663 / 0.434364 (0.009299) | 0.537071 / 0.540337 (-0.003267) | 0.640539 / 1.386936 (-0.746397) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007019 / 0.011353 (-0.004334) | 0.005091 / 0.011008 (-0.005917) | 0.074588 / 0.038508 (0.036080) | 0.032391 / 0.023109 (0.009282) | 0.340548 / 0.275898 (0.064650) | 0.367159 / 0.323480 (0.043679) | 0.005594 / 0.007986 (-0.002392) | 0.004003 / 0.004328 (-0.000325) | 0.073946 / 0.004250 (0.069695) | 0.045921 / 0.037052 (0.008868) | 0.340245 / 0.258489 (0.081756) | 0.397958 / 0.293841 (0.104117) | 0.036539 / 0.128546 (-0.092007) | 0.012258 / 0.075646 (-0.063388) | 0.087406 / 0.419271 (-0.331865) | 0.049276 / 0.043533 (0.005743) | 0.345235 / 0.255139 (0.090096) | 0.361250 / 0.283200 (0.078050) | 0.100757 / 0.141683 (-0.040926) | 1.464644 / 1.452155 (0.012489) | 1.545852 / 1.492716 (0.053136) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.222952 / 0.018006 (0.204945) | 0.434607 / 0.000490 (0.434117) | 0.000438 / 0.000200 (0.000238) | 0.000060 / 0.000054 (0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.028834 / 0.037411 (-0.008577) | 0.107523 / 0.014526 (0.092997) | 0.122077 / 0.176557 (-0.054479) | 0.156574 / 0.737135 (-0.580561) | 0.122917 / 0.296338 (-0.173421) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.417292 / 0.215209 (0.202083) | 4.165980 / 2.077655 (2.088325) | 1.996731 / 1.504120 (0.492611) | 1.802946 / 1.541195 (0.261751) | 1.878456 / 1.468490 (0.409966) | 0.711035 / 4.584777 (-3.873742) | 3.847357 / 3.745712 (0.101644) | 2.088354 / 5.269862 (-3.181508) | 1.344763 / 4.565676 (-3.220913) | 0.086356 / 0.424275 (-0.337919) | 0.012530 / 0.007607 (0.004923) | 0.511693 / 0.226044 (0.285648) | 5.126093 / 2.268929 (2.857165) | 2.490023 / 55.444624 (-52.954602) | 2.180274 / 6.876477 (-4.696202) | 2.221511 / 2.142072 (0.079438) | 0.836348 / 4.805227 (-3.968879) | 0.169554 / 6.500664 (-6.331110) | 0.064555 / 0.075469 (-0.010914) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.293466 / 1.841788 (-0.548321) | 14.785700 / 8.074308 (6.711392) | 13.858493 / 10.191392 (3.667101) | 0.161777 / 0.680424 (-0.518646) | 0.017794 / 0.534201 (-0.516407) | 0.426286 / 0.579283 (-0.152997) | 0.422517 / 0.434364 (-0.011847) | 0.530777 / 0.540337 (-0.009560) | 0.634822 / 1.386936 (-0.752114) |

</details>
</details>

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