mrdbourke/trashify_manual_labelled_images

Load data

import datasets

dataset = datasets.load_dataset("mrdbourke/trashify_manual_labelled_images")
dataset

View a sample

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

# 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

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})