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@@ -58,3 +58,5 @@ in_fashion/dataset_metadata_wild.json filter=lfs diff=lfs merge=lfs -text
 in_fashion/gt_tagging/in_fashion_outshop_tags_dict.json filter=lfs diff=lfs merge=lfs -text
 in_fashion/gt_tagging/manifest.json filter=lfs diff=lfs merge=lfs -text
 in_fashion/gt_tagging/manifest_wild.json filter=lfs diff=lfs merge=lfs -text
+in_fashion/gt_tagging_wild/in_fashion_outshop_tags_dict.json filter=lfs diff=lfs merge=lfs -text
+in_fashion/gt_tagging_wild/manifest.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,3 @@
 ---
 license: mit
-tags:
-- vsd
-pretty_name: VSDFashion
----
+---

in_fashion/gt_tagging_wild/in_fashion_outshop_tags_dict.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed2cc5de0e47e9de2d460a57fcf9f68a89074d69ac628c3286c6fea8b7cf2be3
+size 11836899

in_fashion/gt_tagging_wild/manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4257bfcfc9e867d62844a31c82d3a20db2c71e15b36cef814a13251a8ed2f52
+size 15430033

vsd-fashion.py

@@ -127,6 +127,12 @@ class VSDFashion(datasets.GeneratorBasedBuilder):
             dataset_folder='in_fashion',
             split_folder='gt_tagging_split_closed_catalog',
         ),
+        VSDFashionConfig(
+            name="consumer-catalog_wild_zero_shot",
+            description="Zero shot task for matching a consumer taken photo of a clothing and visually matching with a catalog item. Pretraining on any data is allowed, except consumer photos (queries).",
+            dataset_folder='in_fashion',
+            split_folder='gt_tagging_wild',
+        ),
     ]
 
     DEFAULT_CONFIG_NAME = "in_catalog_retrieval_zero_shot"
@@ -157,16 +163,6 @@ class VSDFashion(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        # urls = _URLS[self.config.name]
-        # data_dir = dl_manager.download_and_extract(urls)
-        # dataset_path = Path(snapshot_download(repo_id=HF_DATASET_ID, repo_type='datasets'))
-        # dataset_path = Path(dl_manager.download_and_extract(_URL + f"/{self.config.dataset_folder}/{self.config.split_folder}"))
         dataset_path = Path(snapshot_download(_HF_DATASET_ID, repo_type='dataset'))
 
         task_data_path = dataset_path/self.config.dataset_folder/self.config.split_folder
@@ -177,9 +173,22 @@ class VSDFashion(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.TEST,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "manifest": task_data_path/'mainifest.json',
-                        "seeds": task_data_path/'seeds.json',
-                        "annotations": task_data_path/'in_fashion_tags_dict.json',
+                        "manifest_file": task_data_path/'mainifest.json',
+                        "seeds_file": task_data_path/'seeds.json',
+                        "annotations_file": task_data_path/'in_fashion_tags_dict.json',
+                        "split": "test",
+                    },
+                ),
+            ] 
+        elif self.config.name == 'consumer-catalog_wild_zero_shot':
+             return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "manifest_file": task_data_path/'mainifest.json',
+                        "seeds_file": task_data_path/'seeds.json',
+                        "annotations_file": task_data_path/'in_fashion_outshop_tags_dict.json',
                         "split": "test",
                     },
                 ),
@@ -190,9 +199,9 @@ class VSDFashion(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "manifest": task_data_path/'mainifest_train.json',
-                        "seeds": task_data_path/'seeds_train.json',
-                        "annotations": task_data_path/'in_fashion_tags_dict_train.json',
+                        "manifest_file": task_data_path/'mainifest_train.json',
+                        "seeds_file": task_data_path/'seeds_train.json',
+                        "annotations_file": task_data_path/'in_fashion_tags_dict_train.json',
                         "split": "train",
                     },
                 ),
@@ -200,20 +209,17 @@ class VSDFashion(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.TEST,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "manifest": task_data_path/'mainifest_test.json',
-                        "seeds": task_data_path/'seeds_test.json',
-                        "annotations": task_data_path/'in_fashion_tags_dict_test.json',
+                        "manifest_file": task_data_path/'mainifest_test.json',
+                        "seeds_file": task_data_path/'seeds_test.json',
+                        "annotations_file": task_data_path/'in_fashion_tags_dict_test.json',
                         "split": "test",
                     },
                 ),
             ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, annotations, **kwargs):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        print("Opening ", annotations)
-        with open(annotations, encoding="utf-8") as f:
+    def _generate_examples(self, annotations_file, **kwargs):
+        with open(annotations_file, encoding="utf-8") as f:
             data = json.load(f)
 
             for key, row in enumerate(data):