dataset uploaded by roboflow2huggingface package

README.md

@@ -3,10 +3,13 @@ task_categories:
 - object-detection
 tags:
 - roboflow
+- roboflow2huggingface
+- Manufacturing
 ---
 
-### Roboflow Dataset Page
-[https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1](https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1?ref=roboflow2huggingface)
+<div align="center">
+  <img width="640" alt="keremberke/forklift-object-detection" src="https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/thumbnail.jpg">
+</div>
 
 ### Dataset Labels
 
@@ -14,6 +17,34 @@ tags:
 ['forklift', 'person']
 ```
 
+
+### Number of Images
+
+```json
+{'test': 42, 'valid': 84, 'train': 295}
+```
+
+
+### How to Use
+
+- Install [datasets](https://pypi.org/project/datasets/):
+
+```bash
+pip install datasets
+```
+
+- Load the dataset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("keremberke/forklift-object-detection", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1](https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1?ref=roboflow2huggingface)
+
 ### Citation
 
 ```
@@ -27,7 +58,7 @@ tags:
     publisher = { Roboflow },
     year = { 2022 },
     month = { mar },
-    note = { visited on 2023-01-01 },
+    note = { visited on 2023-01-15 },
 }
 ```
 

data/test.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d47399ff43cca765cc4c9c5293ce0d6fe8d14f4658e39506bdce2fdadff31003
+oid sha256:9baace711b989e84c731fa3b3e2b45f8a636880d475c2c2d6b91b8cdff803cbe
 size 2771676

data/train.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fb46061800b2175f2a1e5aa2c3136af198c41dfd1ef514f5979b92715e9c307d
+oid sha256:bf5c3364b39160d4d212a454b9a7689f8b89e81eab3ea3da50031c4e7bf92fe0
 size 13533922

data/valid-mini.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94d605e3645a7bd47a91aae22a14cce0b17d40b2c9483aed5ad30be756e0c038
+size 105141

data/valid.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c98ee46aba21d732e1b9b7a7faa64d0910410f9020fa135454dc78087b35ca15
+oid sha256:0f8013a437dc85e8f218b252ce8cffa32b2724c8021cf6b983febd3a9145d53f
 size 3774165

forklift-object-detection.py

@@ -18,21 +18,52 @@ _CITATION = """\
     publisher = { Roboflow },
     year = { 2022 },
     month = { mar },
-    note = { visited on 2023-01-01 },
+    note = { visited on 2023-01-15 },
 }
 """
-_URLS = {
-    "train": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/train.zip",
-    "validation": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid.zip",
-    "test": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/test.zip",
-}
-
 _CATEGORIES = ['forklift', 'person']
 _ANNOTATION_FILENAME = "_annotations.coco.json"
 
 
+class FORKLIFTOBJECTDETECTIONConfig(datasets.BuilderConfig):
+    """Builder Config for forklift-object-detection"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for forklift-object-detection.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FORKLIFTOBJECTDETECTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
 class FORKLIFTOBJECTDETECTION(datasets.GeneratorBasedBuilder):
+    """forklift-object-detection object detection dataset"""
+
     VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        FORKLIFTOBJECTDETECTIONConfig(
+            name="full",
+            description="Full version of forklift-object-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/train.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid.zip",
+                "test": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/test.zip",
+            },
+        ),
+        FORKLIFTOBJECTDETECTIONConfig(
+            name="mini",
+            description="Mini version of forklift-object-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid-mini.zip",
+            },
+        )
+    ]
 
     def _info(self):
         features = datasets.Features(
@@ -59,7 +90,7 @@ class FORKLIFTOBJECTDETECTION(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS)
+        data_files = dl_manager.download_and_extract(self.config.data_urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -92,7 +123,7 @@ class FORKLIFTOBJECTDETECTION(datasets.GeneratorBasedBuilder):
 
         image_id_to_image = {}
         idx = 0
-        
+
         annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
         with open(annotation_filepath, "r") as f:
             annotations = json.load(f)
@@ -100,12 +131,12 @@ class FORKLIFTOBJECTDETECTION(datasets.GeneratorBasedBuilder):
         image_id_to_annotations = collections.defaultdict(list)
         for annot in annotations["annotations"]:
             image_id_to_annotations[annot["image_id"]].append(annot)
-        image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+        filename_to_image = {image["file_name"]: image for image in annotations["images"]}
 
         for filename in os.listdir(folder_dir):
             filepath = os.path.join(folder_dir, filename)
-            if filename in image_id_to_image:
-                image = image_id_to_image[filename]
+            if filename in filename_to_image:
+                image = filename_to_image[filename]
                 objects = [
                     process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
                 ]

split_name_to_num_samples.json

@@ -0,0 +1 @@
+{"test": 42, "valid": 84, "train": 295}