Update download url and improve card of `cats_vs_dogs` dataset (#4523)

* Update dl url and remove file path info

* Update infos

* Improve card

Commit from https://github.com/huggingface/datasets/commit/f561c53767d961a3578ca291d6ca5e4c2c51dab1

README.md

@@ -16,7 +16,9 @@ source_datasets:
 - original
 task_categories:
 - image-classification
-task_ids: []
+task_ids:
+- multi-class-image-classification
+paperswithcode_id: cats-vs-dogs
 ---
 
 # Dataset Card for Cats Vs. Dogs
@@ -50,13 +52,23 @@ task_ids: []
 
 - **Homepage:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765)
 - **Repository:**
-- **Paper:** [Paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf)
-- **Leaderboard:**
+- **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf)
+- **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats)
 - **Point of Contact:**
 
 ### Dataset Summary
 
-A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.
+A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
+
+From the competition page:
+
+> The Asirra data set
+>
+> Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.
+>
+> Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:
+>
+> Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. 
 
 ### Supported Tasks and Leaderboards
 
@@ -64,7 +76,7 @@ A large set of images of cats and dogs. There are 1738 corrupted images that are
 
 ### Languages
 
-English
+English.
 
 ## Dataset Structure
 
@@ -74,9 +86,8 @@ A sample from the training set is provided below:
 
 ```
 {
-  'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/6e1e8c9052e9f3f7ecbcb4b90860668f81c1d36d86cc9606d49066f8da8bfb4f/PetImages/Cat/1.jpg',
   'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>,
-  'label': 0
+  'labels': 0
 }
 ```
 
@@ -84,7 +95,6 @@ A sample from the training set is provided below:
 
 The data instances have the following fields:
 
-- `image_file_path`: a `string` filepath to an image.
 - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
 - `labels`: an `int` classification label.
 
@@ -107,27 +117,37 @@ Class Label Mappings:
 
 ### Curation Rationale
 
-[More Information Needed]
+This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA:
+
+From the competition page:
+
+> Image recognition attacks
+>
+> While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459.
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+This dataset is a subset of the Asirra dataset.
+
+From the competition page:
+
+> Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States.
 
 #### Who are the source language producers?
 
-[More Information Needed]
+The users of [Petfinder.com](https://www.petfinder.com/).
 
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
+The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/).
 
 #### Who are the annotators?
 
-[More Information Needed]
+The users of [Petfinder.com](https://www.petfinder.com/).
 
 ### Personal and Sensitive Information
 
@@ -141,7 +161,11 @@ Class Label Mappings:
 
 ### Discussion of Biases
 
-[More Information Needed]
+From the paper:
+
+> Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This
+makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.”
+
 
 ### Other Known Limitations
 
@@ -159,7 +183,7 @@ Class Label Mappings:
 
 ### Citation Information
 
-```
+```bibtex
 @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
 author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
 title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},

cats_vs_dogs.py

@@ -23,7 +23,7 @@ from datasets.tasks import ImageClassification
 
 logger = datasets.logging.get_logger(__name__)
 
-_URL = "https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip"
+_URL = "https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip"
 
 _HOMEPAGE = "https://www.microsoft.com/en-us/download/details.aspx?id=54765"
 
@@ -44,14 +44,13 @@ _CITATION = """\
 
 
 class CatsVsDogs(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.1")
+    VERSION = datasets.Version("1.0.0")
 
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "image_file_path": datasets.Value("string"),
                     "image": datasets.Image(),
                     "labels": datasets.features.ClassLabel(names=["cat", "dog"]),
                 }
@@ -76,7 +75,6 @@ class CatsVsDogs(datasets.GeneratorBasedBuilder):
                 with open(file, "rb") as f:
                     if b"JFIF" in f.peek(10):
                         yield str(i), {
-                            "image_file_path": file,
                             "image": file,
                             "labels": os.path.basename(os.path.dirname(file)).lower(),
                         }

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.", "citation": "@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,\n    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},\n    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},\n    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n    year = {2007},\n    month = {October},\n    publisher = {Association for Computing Machinery, Inc.},\n    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},\n    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n}\n", "homepage": "https://www.microsoft.com/en-us/download/details.aspx?id=54765", "license": "", "features": {"image_file_path": {"dtype": "string", "id": null, "_type": "Value"}, "image": {"decode": true, "id": null, "_type": "Image"}, "labels": {"num_classes": 2, "names": ["cat", "dog"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "labels"}, "task_templates": [{"task": "image-classification", "image_column": "image", "label_column": "labels"}], "builder_name": "cats_vs_dogs", "config_name": "default", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 7593077, "num_examples": 23410, "dataset_name": "cats_vs_dogs"}}, "download_checksums": {"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip": {"num_bytes": 824894548, "checksum": "f9553e426bd725354ed3a27e3c6920caadb55c835d1ebd880d2e56d3f1fbb22b"}}, "download_size": 824894548, "post_processing_size": null, "dataset_size": 7593077, "size_in_bytes": 832487625}}
+{"default": {"description": "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.", "citation": "@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,\n    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},\n    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},\n    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n    year = {2007},\n    month = {October},\n    publisher = {Association for Computing Machinery, Inc.},\n    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},\n    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n}\n", "homepage": "https://www.microsoft.com/en-us/download/details.aspx?id=54765", "license": "", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "labels": {"num_classes": 2, "names": ["cat", "dog"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "image", "output": "labels"}, "task_templates": [{"task": "image-classification", "image_column": "image", "label_column": "labels"}], "builder_name": "cats_vs_dogs", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3893603, "num_examples": 23422, "dataset_name": "cats_vs_dogs"}}, "download_checksums": {"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip": {"num_bytes": 824887076, "checksum": "b7974bd00a84a99921f36ee4403f089853777b5ae8d151c76a86e64900334af9"}}, "download_size": 824887076, "post_processing_size": null, "dataset_size": 3893603, "size_in_bytes": 828780679}}