Update parquet files

processed/valid/dataset.arrow → Kaludi--data-food-category-classification/imagefolder-train.parquet

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raw/image_folders/train/training/Dessert/44.jpg → Kaludi--data-food-category-classification/imagefolder-validation.parquet

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-size 56272
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README.md

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----
-task_categories:
-- image-classification
-
----
-# Dataset for project: food-category-classification
-
-## Dataset Description
-
-This dataset is for project food-category-classification.
-
-### Languages
-
-The BCP-47 code for the dataset's language is unk.
-
-## Dataset Structure
-
-### Data Instances
-
-A sample from this dataset looks as follows:
-
-```json
-[
-  {
-    "image": "<512x512 RGB PIL image>",
-    "target": 0
-  },
-  {
-    "image": "<512x512 RGB PIL image>",
-    "target": 0
-  }
-]
-```
-
-### Dataset Fields
-
-The dataset has the following fields (also called "features"):
-
-```json
-{
-  "image": "Image(decode=True, id=None)",
-  "target": "ClassLabel(names=['Bread', 'Dairy product', 'Dessert', 'Egg', 'Fried food', 'Meat', 'Noodles-Pasta', 'Rice', 'Seafood', 'Soup', 'Vegetable-Fruit'], id=None)"
-}
-```
-
-### Dataset Splits
-
-This dataset is split into a train and validation split. The split sizes are as follow:
-
-| Split name   | Num samples         |
-| ------------ | ------------------- |
-| train        | 1210 |
-| valid        | 275 |

processed/dataset_dict.json

@@ -1 +0,0 @@
-{"splits": ["train", "valid"]}

processed/train/dataset.arrow

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processed/train/dataset_info.json

@@ -1,35 +0,0 @@
-{
-  "citation": "",
-  "description": "AutoTrain generated dataset",
-  "features": {
-    "image": {
-      "_type": "Image"
-    },
-    "target": {
-      "names": [
-        "Bread",
-        "Dairy product",
-        "Dessert",
-        "Egg",
-        "Fried food",
-        "Meat",
-        "Noodles-Pasta",
-        "Rice",
-        "Seafood",
-        "Soup",
-        "Vegetable-Fruit"
-      ],
-      "_type": "ClassLabel"
-    }
-  },
-  "homepage": "",
-  "license": "",
-  "splits": {
-    "train": {
-      "name": "train",
-      "num_bytes": 85527433,
-      "num_examples": 1210,
-      "dataset_name": null
-    }
-  }
-}

processed/train/state.json

@@ -1,17 +0,0 @@
-{
-  "_data_files": [
-    {
-      "filename": "dataset.arrow"
-    }
-  ],
-  "_fingerprint": "547bcac53079f23f",
-  "_format_columns": [
-    "image",
-    "target"
-  ],
-  "_format_kwargs": {},
-  "_format_type": null,
-  "_indexes": {},
-  "_output_all_columns": false,
-  "_split": null
-}

processed/valid/dataset_info.json

@@ -1,35 +0,0 @@
-{
-  "citation": "",
-  "description": "AutoTrain generated dataset",
-  "features": {
-    "image": {
-      "_type": "Image"
-    },
-    "target": {
-      "names": [
-        "Bread",
-        "Dairy product",
-        "Dessert",
-        "Egg",
-        "Fried food",
-        "Meat",
-        "Noodles-Pasta",
-        "Rice",
-        "Seafood",
-        "Soup",
-        "Vegetable-Fruit"
-      ],
-      "_type": "ClassLabel"
-    }
-  },
-  "homepage": "",
-  "license": "",
-  "splits": {
-    "valid": {
-      "name": "valid",
-      "num_bytes": 21165102,
-      "num_examples": 275,
-      "dataset_name": null
-    }
-  }
-}

processed/valid/state.json

@@ -1,17 +0,0 @@
-{
-  "_data_files": [
-    {
-      "filename": "dataset.arrow"
-    }
-  ],
-  "_fingerprint": "b586fe5a65d0452b",
-  "_format_columns": [
-    "image",
-    "target"
-  ],
-  "_format_kwargs": {},
-  "_format_type": null,
-  "_indexes": {},
-  "_output_all_columns": false,
-  "_split": null
-}