Update parquet files

processed/valid/dataset.arrow → Kaludi--data-csgo-weapon-classification/imagefolder-test.parquet

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processed/train/dataset.arrow → Kaludi--data-csgo-weapon-classification/imagefolder-train.parquet

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raw/image_folders/train/train/AK-47/AK470001.jpg → Kaludi--data-csgo-weapon-classification/imagefolder-validation.parquet

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README.md

@@ -1,53 +0,0 @@
----
-task_categories:
-- image-classification
-
----
-# Dataset for project: csgo-weapon-classification
-
-## Dataset Description
-
-This dataset has for project csgo-weapon-classification was collected with the help of a bulk google image downloader.
-
-### 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": "<1768x718 RGB PIL image>",
-    "target": 0
-  },
-  {
-    "image": "<716x375 RGBA 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=['AK-47', 'AWP', 'Famas', 'Galil-AR', 'Glock', 'M4A1', 'M4A4', 'P-90', 'SG-553', 'UMP', 'USP'], 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        | 1100 |
-| valid        | 275 |

processed/dataset_dict.json

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

processed/train/dataset_info.json

@@ -1,35 +0,0 @@
-{
-  "citation": "",
-  "description": "AutoTrain generated dataset",
-  "features": {
-    "image": {
-      "_type": "Image"
-    },
-    "target": {
-      "names": [
-        "AK-47",
-        "AWP",
-        "Famas",
-        "Galil-AR",
-        "Glock",
-        "M4A1",
-        "M4A4",
-        "P-90",
-        "SG-553",
-        "UMP",
-        "USP"
-      ],
-      "_type": "ClassLabel"
-    }
-  },
-  "homepage": "",
-  "license": "",
-  "splits": {
-    "train": {
-      "name": "train",
-      "num_bytes": 245114734,
-      "num_examples": 1100,
-      "dataset_name": null
-    }
-  }
-}

processed/train/state.json

@@ -1,17 +0,0 @@
-{
-  "_data_files": [
-    {
-      "filename": "dataset.arrow"
-    }
-  ],
-  "_fingerprint": "f08bd56542ad550c",
-  "_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": [
-        "AK-47",
-        "AWP",
-        "Famas",
-        "Galil-AR",
-        "Glock",
-        "M4A1",
-        "M4A4",
-        "P-90",
-        "SG-553",
-        "UMP",
-        "USP"
-      ],
-      "_type": "ClassLabel"
-    }
-  },
-  "homepage": "",
-  "license": "",
-  "splits": {
-    "valid": {
-      "name": "valid",
-      "num_bytes": 78604575,
-      "num_examples": 275,
-      "dataset_name": null
-    }
-  }
-}

processed/valid/state.json

@@ -1,17 +0,0 @@
-{
-  "_data_files": [
-    {
-      "filename": "dataset.arrow"
-    }
-  ],
-  "_fingerprint": "702c9ccde4799944",
-  "_format_columns": [
-    "image",
-    "target"
-  ],
-  "_format_kwargs": {},
-  "_format_type": null,
-  "_indexes": {},
-  "_output_all_columns": false,
-  "_split": null
-}