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---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: int64
- name: annotations
sequence:
- name: file_name
dtype: string
- name: image_id
dtype: int64
- name: category_id
dtype:
class_label:
names:
'0': bin
'1': hand
'2': not_bin
'3': not_hand
'4': not_trash
'5': trash
'6': trash_arm
- name: bbox
sequence: float32
length: 4
- name: iscrowd
dtype: int64
- name: area
dtype: float32
- name: label_source
dtype: string
- name: image_source
dtype: string
splits:
- name: train
num_bytes: 1022952485.728
num_examples: 1128
download_size: 1026537298
dataset_size: 1022952485.728
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
## Load data
```python
import datasets
dataset = datasets.load_dataset("mrdbourke/trashify_manual_labelled_images")
dataset
```
## View a sample
```python
dataset["train"][0]
```
Output:
```
{'image': <PIL.Image.Image image mode=RGB size=960x1280>,
'image_id': 292,
'annotations': {'file_name': ['00347467-13f1-4cb9-94aa-4e4369457e0c.jpeg',
'00347467-13f1-4cb9-94aa-4e4369457e0c.jpeg'],
'image_id': [292, 292],
'category_id': [1, 0],
'bbox': [[523.7000122070312,
545.0999755859375,
402.79998779296875,
336.1000061035156],
[10.399999618530273,
163.6999969482422,
943.4000244140625,
1101.9000244140625]],
'iscrowd': [0, 0],
'area': [135381.078125, 1039532.4375]},
'label_source': 'manual_prodigy_label',
'image_source': 'manual_taken_photo'}
```
**Note:** Boxes in "bbox" key are in `XYWH` format or `[x_min, y_min, box_width, box_height]`. If you'd like them in `XYXY` format, you'll have to convert them.
## Get categories
```python
# Get the categories from the dataset
# Note: this requires the dataset to have been uploaded with this feature setup
categories = dataset["train"].features["annotations"].feature["category_id"]
# Get the names attribute
categories.names
>>> ['bin', 'hand', 'not_bin', 'not_hand', 'not_trash', 'trash', 'trash_arm']
```
## Create label2id and id2label
```python
id2label = {i: class_name for i, class_name in enumerate(categories.names)}
label2id = {value: key for key, value in id2label.items()}
id2label, label2id
```
Output:
```
({0: 'bin',
1: 'hand',
2: 'not_bin',
3: 'not_hand',
4: 'not_trash',
5: 'trash',
6: 'trash_arm'},
{'bin': 0,
'hand': 1,
'not_bin': 2,
'not_hand': 3,
'not_trash': 4,
'trash': 5,
'trash_arm': 6})
``` |