feat: upload script docs: readme

README.md

@@ -0,0 +1,18 @@
+---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: mask
+    dtype: image
+  - name: bboxes
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 56575701
+    num_examples: 46
+  download_size: 56584366
+  dataset_size: 56575701
+---

face_segmentation.py → helmet_detection.py

@@ -3,7 +3,7 @@ import pandas as pd
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-title = {face_segmentation},
+title = {helmet_detection},
 author = {TrainingDataPro},
 year = {2023}
 }
@@ -14,7 +14,7 @@ An example of a dataset that we've collected for a photo edit App.
 The dataset includes 20 selfies of people (man and women)
 in segmentation masks and their visualisations.
 """
-_NAME = 'face_segmentation'
+_NAME = 'helmet_detection'
 
 _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
 
@@ -30,11 +30,10 @@ class FaceSegmentation(datasets.GeneratorBasedBuilder):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features({
+                'id': datasets.Value('string'),
                 'image': datasets.Image(),
                 'mask': datasets.Image(),
-                'id': datasets.Value('string'),
-                'gender': datasets.Value('string'),
-                'age': datasets.Value('int8')
+                'bboxes': datasets.Value('string')
             }),
             supervised_keys=None,
             homepage=_HOMEPAGE,
@@ -57,11 +56,12 @@ class FaceSegmentation(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, images, masks, annotations):
-        annotations_df = pd.read_csv(annotations, sep=';')
+        annotations_df = pd.read_csv(annotations)
 
         for idx, ((image_path, image),
                   (mask_path, mask)) in enumerate(zip(images, masks)):
             yield idx, {
+                'id': annotations_df['image_id'].iloc[idx],
                 "image": {
                     "path": image_path,
                     "bytes": image.read()
@@ -70,7 +70,5 @@ class FaceSegmentation(datasets.GeneratorBasedBuilder):
                     "path": mask_path,
                     "bytes": mask.read()
                 },
-                'id': annotations_df['id'].iloc[idx],
-                'gender': annotations_df['gender'].iloc[idx],
-                'age': annotations_df['age'].iloc[idx]
+                'bboxes': annotations_df['bboxes'].iloc[idx]
             }