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README.md

@@ -1,175 +1,145 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': test
-          '1': train
-  splits:
-  - name: train
-    num_bytes: 441562705.801
-    num_examples: 21159
-  - name: test
-    num_bytes: 80207072.542
-    num_examples: 3841
-  download_size: 572690049
-  dataset_size: 521769778.343
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
-
-# Dataset Documentation
-
-## Overview
-
-This dataset is designed to support machine learning and data analysis tasks. It consists of two compressed archives: `train.tar` and `test.tar`. These archives contain data for training and testing purposes, respectively. The dataset is structured to facilitate easy integration into machine learning pipelines and other data-driven workflows.
-
----
-
-## Dataset Contents
-
-### 1. `train.tar`
-The `train.tar` archive contains the training data required to build and train machine learning models. This data is typically used to teach models to recognize patterns, make predictions, or classify data points.
-
-- **Purpose**: Training machine learning models.
-- **Contents**: The archive includes multiple files (or directories) that represent the training dataset. Each file may correspond to a specific data sample, feature set, or label.
-
-### 2. `test.tar`
-The `test.tar` archive contains the testing data used to evaluate the performance of trained models. This data is separate from the training set to ensure unbiased evaluation.
-
-- **Purpose**: Testing and validating machine learning models.
-- **Contents**: Similar to the training archive, this archive includes files (or directories) that represent the testing dataset.
-
----
-
-## File Structure
-
-After extracting the `.tar` files, the dataset will have the following structure:
-
-```
-dataset/
-├── train/
-│   ├── file1.ext
-│   ├── file2.ext
-│   └── ...
-└── test/
-    ├── file1.ext
-    ├── file2.ext
-    └── ...
-```
-
-- **`train/`**: Contains training data files.
-- **`test/`**: Contains testing data files.
-
----
-
-## How to Use the Dataset
-
-### Step 1: Extract the Archives
-To access the dataset, you need to extract the contents of the `.tar` files. Use the following commands:
-
-```bash
-tar -xvf train.tar
-tar -xvf test.tar
-```
-
-This will create two directories: `train/` and `test/`.
-
-### Step 2: Load the Data
-Once extracted, you can load the data into your preferred programming environment. For example, in Python:
-
-```python
-import os
-
-# Define paths
-train_path = "train/"
-test_path = "test/"
-
-# List files in the training directory
-train_files = os.listdir(train_path)
-print("Training Files:", train_files)
-
-# List files in the testing directory
-test_files = os.listdir(test_path)
-print("Testing Files:", test_files)
-```
-
-### Step 3: Integrate with Your Workflow
-You can now use the data for training and testing machine learning models. Ensure that you preprocess the data as needed (e.g., normalization, feature extraction, etc.).
-
----
-
-## Dataset Characteristics
-
-- **Size**: The size of the dataset depends on the contents of the `train.tar` and `test.tar` archives.
-- **Format**: The files within the archives may be in formats such as `.csv`, `.txt`, `.json`, or others, depending on the dataset's design.
-- **Labels**: If the dataset is labeled, the labels will typically be included in the training and testing files or in a separate metadata file.
-
----
-
-## Best Practices
-
-1. **Data Splitting**: Ensure that the training and testing data are not mixed to maintain the integrity of model evaluation.
-2. **Preprocessing**: Apply appropriate preprocessing steps to the data, such as cleaning, normalization, or augmentation.
-3. **Version Control**: If you modify the dataset, maintain version control to track changes and ensure reproducibility.
-
----
-
-## Licensing and Usage
-
-Please review the licensing terms associated with this dataset before use. Ensure compliance with any restrictions or requirements.
-
----
-
-## Citation
-
-If you use this dataset in your research or project, please cite it as follows:
-
-```
-[Dataset Name]. Provided by [Dataset Provider]. Retrieved from [Source URL].
-```
-
----
-
-## Frequently Asked Questions (FAQ)
-
-### 1. How do I extract the `.tar` files?
-Use the `tar` command in a terminal or a file extraction tool that supports `.tar` archives.
-
-### 2. What format are the data files in?
-The format of the data files depends on the specific dataset. Common formats include `.csv`, `.txt`, `.json`, and others.
-
-### 3. Can I use this dataset for commercial purposes?
-Refer to the licensing section to determine whether commercial use is permitted.
-
----
-
-## Support
-
-If you encounter any issues or have questions about the dataset, please contact the dataset provider or refer to the official documentation.
-
----
-
-## Acknowledgments
-
-We would like to thank the contributors and maintainers of this dataset for their efforts in creating and sharing this resource.
-
----
-
-## Change Log
-
-- **Version 1.0**: Initial release of the dataset.
-
----
-
+# Dataset Documentation
+
+## Overview
+
+This dataset is designed to support machine learning and data analysis tasks. It consists of two compressed archives: `train.tar` and `test.tar`. These archives contain data for training and testing purposes, respectively. The dataset is structured to facilitate easy integration into machine learning pipelines and other data-driven workflows.
+
+---
+
+## Dataset Contents
+
+### 1. `train.tar`
+The `train.tar` archive contains the training data required to build and train machine learning models. This data is typically used to teach models to recognize patterns, make predictions, or classify data points.
+
+- **Purpose**: Training machine learning models.
+- **Contents**: The archive includes multiple files (or directories) that represent the training dataset. Each file may correspond to a specific data sample, feature set, or label.
+
+### 2. `test.tar`
+The `test.tar` archive contains the testing data used to evaluate the performance of trained models. This data is separate from the training set to ensure unbiased evaluation.
+
+- **Purpose**: Testing and validating machine learning models.
+- **Contents**: Similar to the training archive, this archive includes files (or directories) that represent the testing dataset.
+
+---
+
+## File Structure
+
+After extracting the `.tar` files, the dataset will have the following structure:
+
+```
+dataset/
+├── train/
+│   ├── file1.ext
+│   ├── file2.ext
+│   └── ...
+└── test/
+    ├── file1.ext
+    ├── file2.ext
+    └── ...
+```
+
+- **`train/`**: Contains training data files.
+- **`test/`**: Contains testing data files.
+
+---
+
+## How to Use the Dataset
+
+### Step 1: Extract the Archives
+To access the dataset, you need to extract the contents of the `.tar` files. Use the following commands:
+
+```bash
+tar -xvf train.tar
+tar -xvf test.tar
+```
+
+This will create two directories: `train/` and `test/`.
+
+### Step 2: Load the Data
+Once extracted, you can load the data into your preferred programming environment. For example, in Python:
+
+```python
+import os
+
+# Define paths
+train_path = "train/"
+test_path = "test/"
+
+# List files in the training directory
+train_files = os.listdir(train_path)
+print("Training Files:", train_files)
+
+# List files in the testing directory
+test_files = os.listdir(test_path)
+print("Testing Files:", test_files)
+```
+
+### Step 3: Integrate with Your Workflow
+You can now use the data for training and testing machine learning models. Ensure that you preprocess the data as needed (e.g., normalization, feature extraction, etc.).
+
+---
+
+## Dataset Characteristics
+
+- **Size**: The size of the dataset depends on the contents of the `train.tar` and `test.tar` archives.
+- **Format**: The files within the archives may be in formats such as `.csv`, `.txt`, `.json`, or others, depending on the dataset's design.
+- **Labels**: If the dataset is labeled, the labels will typically be included in the training and testing files or in a separate metadata file.
+
+---
+
+## Best Practices
+
+1. **Data Splitting**: Ensure that the training and testing data are not mixed to maintain the integrity of model evaluation.
+2. **Preprocessing**: Apply appropriate preprocessing steps to the data, such as cleaning, normalization, or augmentation.
+3. **Version Control**: If you modify the dataset, maintain version control to track changes and ensure reproducibility.
+
+---
+
+## Licensing and Usage
+
+Please review the licensing terms associated with this dataset before use. Ensure compliance with any restrictions or requirements.
+
+---
+
+## Citation
+
+If you use this dataset in your research or project, please cite it as follows:
+
+```
+cat-dog. Provided by programersalar.
+```
+
+---
+
+## Frequently Asked Questions (FAQ)
+
+### 1. How do I extract the `.tar` files?
+Use the `tar` command in a terminal or a file extraction tool that supports `.tar` archives.
+
+### 2. What format are the data files in?
+The format of the data files depends on the specific dataset. Common formats include `.csv`, `.txt`, `.json`, and others.
+
+### 3. Can I use this dataset for commercial purposes?
+Refer to the licensing section to determine whether commercial use is permitted.
+
+---
+
+## Support
+
+If you encounter any issues or have questions about the dataset, please contact the dataset provider or refer to the official documentation.
+
+---
+
+## Acknowledgments
+
+We would like to thank the contributors and maintainers of this dataset for their efforts in creating and sharing this resource.
+
+---
+
+## Change Log
+
+- **Version 1.0**: Initial release of the dataset.
+
+---
+
 Thank you for using this dataset! We hope it proves valuable for your projects and research.

data.py

@@ -0,0 +1,6 @@
+from datasets import load_dataset
+
+dataset = load_dataset("imagefolder", data_dir="E:/Dataest/cat_dog_images/data")
+
+
+dataset.push_to_hub("ProgramerSalar/cat-dog-image")

data/test.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e499060e394cec289e5fdd41ff860b61e9fb7f0cd1b79afde834be639a4adc01
+size 91773952

data/train.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:caa62c9544b630a8a2d9f8857b540cfceb24b966c355d8e6c1698ee791167c52
+size 499316736