Datasets:
File size: 2,826 Bytes
0f37849 e67045c 0f37849 e67045c 0f37849 e67045c 0f37849 e67045c 0f37849 e67045c 0f37849 e67045c 0f37849 e67045c 0f37849 |
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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
---
license: apache-2.0
dataset_info:
features:
- name: example_id
dtype: string
- name: watchface_id
dtype: string
- name: watch_time
dtype: string
- name: time_format
dtype: string
- name: shows_seconds
dtype: bool
- name: hour_visible
dtype: int32
- name: minute_visible
dtype: int32
- name: second_visible
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 125130577
num_examples: 800269
- name: validation
num_bytes: 4001351
num_examples: 25600
download_size: 59060771
dataset_size: 129131928
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# PixMo-Clocks
PixMo-Clocks is a collection of synthetic watch faces and time annotations.
The data was created using facer.io.
The repo supplies the meta-data needed to generate the data but does not directly contain the images
PixMo-Clocks is a part of the PixMo dataset collection and was used to train the [Molmo family of models](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19)
Quick links:
- 📃 [Paper](https://molmo.allenai.org/paper.pdf)
- 🎥 [Blog with Videos](https://molmo.allenai.org/blog)
## Loading
```python
clocks_dataset = datasets.load_dataset("allenai/pixmo-clocks")
```
## Data Format
The data includes:
- `watchface_id: The watchface id to use to generate the image
- `watch_time`: The time the watch was set to, not all the details of the time will be visible on the watchface, so the
the following two fields are needed.
- `time_format`: The format of the watch, can be:
- `"No time visible`": The time cannot be read at all, we still include the examples as no-answer training examples
- `"Without AM/PM`": AM/PM cannot be determined
- `"With AM/PM`": AM/PM can be determined (either because the watch shows military time, or shows a AM/PM indicator somehow)
- `shows_seconds`: Whether seconds are shown
For convenience, this dataset includes a few fields derived from this data about what is visible on the watch:
- `hour_visible`: The hour visible on the watch, between 0 and 23, -1 means not visible, 0 is 12:00am
- `minute_visible`: The minute the watch should be set to, between 0 and 59, -1 means not visible
- `second_visible`: The second the watch should be set to, between -1 and 59, -1 means not visible
- `text`: A text string of the time
## Generating Images
We will include a script to generate the images as part of our code release.
## Splits
The data is divided into validation and train splits. These splits are ``unofficial`` because we do not use this data for evaluation anyway. However,
they reflect what was used when training the Molmo models, which were only trained on the train split. |