Dataset

This dataset was created by Alexis Cook

Contents

https://www.kaggle.com/datasets/muzammilaliveltech/farm-harmful-animals-dataset

this dataset is not mine, it was uploaded to Kaggle by MUZAMMIL ALI VELTECH under CC0: Public Domain. This Roboflow project was made as an attempt to use the dataset after having issue trying to import in Jupyter Notebook from Kaggle

[Context]

The official Meta-Kaggle dataset contains the Users.csv file which contains Username, DisplayName, RegisterDate, and PerformanceTier fields but doesn't contain location data of the Kaggle Users. This dataset augments that data with additional country and region information.

[Note]

I haven't included the username and displayname values on purpose, just the userid to be joined back to the Meta-Kaggle official Users.csv file.

[Limitations]

It is possible that some users haven't inputted their details when the scraper went through their accounts and thus have missing data. Another possibility is that users may have updated their info after the scraper went through their accounts, thus resulting in inconsistencies.

[How I defined active in this dataset]

• Users that have received an upvote in the forums, datasets, or notebooks
• Users that have given an upvote in the forums, datasets, or notebooks
• Users that have created a thread, a forum post, a notebook, or a dataset
• Users that made a competition submission
• Users that exist in the Meta-Kaggle Users dataset
• Date cut-off of Jan 01, 2019

[Update]

• 15-Feb-2024- Since the Kaggle member's profile page update, the scrapers arent working anymore as the UI layout has changed. Will fix this when we get the time.

Clean the data, Preprocess it and then make predictions. Try and Learn.

Mirror of external ZIP: https://aic-data.ledo.io.vn/Videos_L22_a.zip

Uploaded via Kaggle Notebook (one-file-per-dataset).

Custom Training with YOLOv7 🔥

Some Important links

• Model Inference🤖
• 🚀Training Yolov7 on Kaggle
• Weight and Biases 🐝
• HuggingFace 🤗 Model Repo

Contact Information

• Name - Owais Ahmad
• Phone - +91-9515884381
• Email - owaiskhan9654@gmail.com
• Portfolio - https://owaiskhan9654.github.io/

Objective

To Showcase custom Object Detection on the Given Dataset to train and Infer the Model using newly launched YoloV7.

Data Acquisition

The goal of this task is to train a model that can localize and classify each instance of Person and Car as accurately as possible.

• Link to the Downloadable Dataset

from IPython.display import Markdown, display

display(Markdown("../input/Car-Person-v2-Roboflow/README.roboflow.txt"))

Custom Training with YOLOv7 🔥

In this Notebook, I have processed the images with RoboFlow because in COCO formatted dataset was having different dimensions of image and Also data set was not splitted into different Format. To train a custom YOLOv7 model we need to recognize the objects in the dataset. To do so I have taken the following steps:

• Export the dataset to YOLOv7
• Train YOLOv7 to recognize the objects in our dataset
• Evaluate our YOLOv7 model's performance
• Run test inference to view performance of YOLOv7 model at work

📦 YOLOv7

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/car-person-2.PNG" width=800>

Image Credit - jinfagang

Step 1: Install Requirements

!git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
%cd yolov7
!pip install -qr requirements.txt
!pip install -q roboflow

Downloading YOLOV7 starting checkpoint

!wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"

import os
import glob
import wandb
import torch
from roboflow import Roboflow
from kaggle_secrets import UserSecretsClient
from IPython.display import Image, clear_output, display # to display images



print(f"Setup complete. Using torch {torch._version_} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")

https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67">

I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!

YOLOv7-Car-Person-Custom

try:
  user_secrets = UserSecretsClient()
  wandb_api_key = user_secrets.get_secret("wandb_api")
  wandb.login(key=wandb_api_key)
  anonymous = None
except:
  wandb.login(anonymous='must')
  print('To use your W&B account,
Go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. 
Get your W&B access token from here: https://wandb.ai/authorize')
  
  
  
wandb.init(project="YOLOvR",name=f"7. YOLOv7-Car-Person-Custom-Run-7")

Step 2: Assemble Our Dataset

https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="">

In order to train our custom model, we need to assemble a dataset of representative images with bounding box annotations around the objects that we want to detect. And we need our dataset to be in YOLOv7 format.

In Roboflow, We can choose between two paths:

• Convert an existing Coco dataset to YOLOv7 format. In Roboflow it supports over 30 formats object detection formats for conversion.
• Uploading only these raw images and annotate them in Roboflow with Roboflow Annotate.

Version v2 Aug 12, 2022 Looks like this.

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Roboflow.PNG" alt="">

user_secrets = UserSecretsClient()
roboflow_api_key = user_secrets.get_secret("roboflow_api")

rf = Roboflow(api_key=roboflow_api_key)
project = rf.workspace("owais-ahmad").project("custom-yolov7-on-kaggle-on-custom-dataset-rakiq")
dataset = project.version(2).download("yolov7")

Step 3: Training Custom pretrained YOLOv7 model

Here, I am able to pass a number of arguments: - img: define input image size - batch: determine

Dataset Creation

The notebook uploaded here is used to convert the audio and image into tensor files which will be used by the training script to train the Image Decoder Module. The Dataset used was "https://www.kaggle.com/datasets/jorvan/image-audio-pairs-1-of-3". This dataset must be imported into the environment (Eg. Kaggle) and the path BASE_DATA_PATH must be updated in the notebook

Reddit: /r/pokemon

Exploring Post Popularity, User Engagement and Topic Disscussion

By Reddit [source]

About this dataset

This Kaggle dataset provides a unique opportunity to explore the ongoing conversations and discussions of the popular Pokémon franchise across Reddit communities. It contains over a thousand entries compiled from posts and comments made by avid Pokémon fans, providing valuable insights into post popularity, user engagement, and topic discussion. With these comprehensive data points including post title, score, post ID link URL, number of comments and date & time created along with body text and timestamp – powerful analysis can be conducted to assess how trends in Pokémon-related activities are evolving over time. So why not dive deep into this fascinating world of Poké-interactions? Follow us as we navigate through the wide range of interesting topics being discussed on Reddit about this legendary franchise!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains over a thousand entries of user conversations related to the Pokémon community posted and commented on Reddit. By using this dataset, you can explore the popularity of Pokémon related topics, the level of user engagement, and how user interactions shape the discussion around each topic. To do so, you’ll want to focus on columns such as title, score, url, comms_num (number of comments on a post), created (date and time when post was created) and timestamp.
For starters you can start by looking at how many posts have been made about certain topics by using “title” column as a keyword search bar – e.g., ‘Magikarp’ or ‘Team Rocket’ – to see just how many posts have been about them in total. With this data in mind, you could consider what makes popular posts become popular and look at the number of upvotes from users (stored in “score”)– i.e., what posts caught people's attention? Beyond upvotes however is downvotes - can these be taken into account when it comes to gauging popularity? One could also take into consideration user engagement by looking at comms_num as it contains information regarding number of comments left for each post - does an increase in comments lead to an increase in upvotes?
Additionally one could examine how posts were communicated with users by reading into body texts stored under 'body'. Through this information users can create insights into overall discussion per topic: are they conversational or argumentative? Are there underlying regional trends taking place among commenters who place emphasis on different elements regarding their pokemon-related discussions?
This opens up possibilities for further investigations into understanding pokemon-related phenomena through Reddit discussion; finding out what makes certain topics prevalent while others stay obscure; seeing where our World Regions lay within certain conversations; all while understanding specific nuances within conversation trees between commenters!

Research Ideas

• Analyzing the influence of post upvotes in user engagement and conversation outcomes
• Investigating the frequency of topics discussed in Pokémon related conversations
• Examining the correlation between post score and number of comments on each post

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: pokemon.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments the post has received. (Integer) | | created | The date and time the post was created. (DateTime) | | body | The body text of the post. (String) | | timestamp | The timestamp of the post. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

This dataset contains different variants of the RoBERTa and XLM-RoBERTa model by Meta AI available on Hugging Face's model repository.

By making it a dataset, it is significantly faster to load the weights since you can directly attach a Kaggle dataset to the notebook rather than downloading the data every time. See the speed comparison notebook. Another benefit of loading models as a dataset is that it can be used in competitions that require internet access to be "off".

For more information on usage visit the roberta hugging face docs and the xlm-roberta hugging face docs.

Usage

To use this dataset, attach it to your notebook and specify the path to the dataset. For example:

from transformers import AutoTokenizer, AutoModelForPreTraining
​
MODEL_DIR = "/kaggle/input/huggingface-roberta/"
​
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR + "roberta-base")
model = AutoModelForPreTraining.from_pretrained(MODEL_DIR + "roberta-base")

Acknowledgements All the copyrights and IP relating to RoBERTa and XLM-RoBERTa belong to the original authors (Liu et al. and Conneau et al.) and Meta AI. All copyrights relating to the transformers library belong to Hugging Face. Please reach out directly to the authors if you have questions regarding licenses and usage.

This dataset has been processed from the Smart meters in London available on Kaggle. The original dataset contains information on households, and the meter readings can be aggregated at various levels - household, blocks, and the entire city. However, due to long processing times and high memory usage during aggregation, this dataset provides processed results that can be directly used for analysis or modeling.

To explore and model the dataset, an example notebook is available at the Github link.

Context

• Giba has introduced hybrid approach which use other notebooks result for better performance: https://www.kaggle.com/titericz/h-m-ensembling-how-to/notebook
• Altu has a new improved version with lstm model: https://www.kaggle.com/code/atulverma/h-m-ensembling-with-lstm

But you need to download other notebooks result, then upload it if you want to use within your notebook. So i create this dataset for anyone who want to use directly notebook result without download/upload. Please upvote if it help you

Content

This dataset contain 5 results as input using for a hybrid approach in this notebook: * https://www.kaggle.com/titericz/h-m-ensembling-how-to/notebook. * https://www.kaggle.com/code/atulverma/h-m-ensembling-with-lstm

If you want to use this notebook but can't access to private dataset, please add my dataset to your notebook, than change file path. It has 5 files: * submissio_byfone_chris.csv: Submission result from: https://www.kaggle.com/lichtlab/0-0226-byfone-chris-combination-approach
* submission_exponential_decay.csv: Submission result from: https://www.kaggle.com/tarique7/hnm-exponential-decay-with-alternate-items/notebook * submission_trending.csv: Submission result from: https://www.kaggle.com/lunapandachan/h-m-trending-products-weekly-add-test/notebook * submission_sequential_model.csv: Submission result from: https://www.kaggle.com/code/astrung/sequential-model-fixed-missing-last-item/notebook * submission_sequential_with_item_feature.csv: Submission result from: https://www.kaggle.com/code/astrung/lstm-model-with-item-infor-fix-missing-last-item/notebook

This dataset is generated for the purpose of analyzing furniture sales data using multiple regression techniques. It contains 2,500 rows with 15 columns, including 7 numerical columns and 7 categorical columns, along with a target variable (revenue) which represents the total revenue generated from furniture sales. The dataset captures various aspects of furniture sales, such as pricing, cost, sales volume, discount percentage, inventory levels, delivery time, and different categorical attributes like furniture type, material, color, and store location.

Guys please upload your notebook of this dataset so that others can also learn from your work

Mirror of external ZIP: https://aic-data.ledo.io.vn/media-info-aic25-b1.zip

Uploaded via Kaggle Notebook (one-file-per-dataset).

Mirror of external ZIP: https://aic-data.ledo.io.vn/clip-features-32-aic25-b1.zip

Uploaded via Kaggle Notebook (one-file-per-dataset).

✅ Step 1: Mount to Dataset

Search for my dataset pytorch-models and add it — this will mount it at:

/kaggle/input/pytorch-models/

✅ Step 2: Check file paths Once mounted, the four files will be available at:

/kaggle/input/pytorch-models/base_models.py
/kaggle/input/pytorch-models/ext_base_models.py
/kaggle/input/pytorch-models/ext_hybrid_models.py
/kaggle/input/pytorch-models/hybrid_models.py

✅ Step 3: Copy files to working directory To make them importable, copy the .py files to your notebook’s working directory (/kaggle/working/):

import shutil

shutil.copy('/kaggle/input/pytorch-models/base_models.py', '/kaggle/working/')
shutil.copy('/kaggle/input/pytorch-models/ext_base_models.py', '/kaggle/working/')
shutil.copy('/kaggle/input/pytorch-models/ext_hybrid_models.py', '/kaggle/working/')
shutil.copy('/kaggle/input/pytorch-models/hybrid_models.py', '/kaggle/working/')

✅ Step 4: Import your modules Now that they are in the working directory, you can import them like normal:

import base_models
import ext_base_models
import ext_hybrid_models
import hybrid_models

Or, if you only want to import specific classes or functions:

from base_models import YourModelClass
from ext_base_models import AnotherModelClass

✅ Step 5: Use the models You can now initialize and use the models/classes/functions defined inside each file:

model = base_models.YourModelClass()
output = model(input_data)

Amazon Scrapping Dataset; 1. Import libraries 2. Connect to the website 3. Import CSV and datetime 4. Import pandas 5. Appending dataset to CSV 6. Automation Dataset updated 7. Timers setup 8. Email notification

MIntRec stands for multimodal intent recognition. The benchmark dataset is first introduced at ACM MM 2022: https://dl.acm.org/doi/10.1145/3503161.3547906. The uploaded version is a latest second version which is introduced in ICLR 2024: https://openreview.net/forum?id=nY9nITZQjc.

More details can be found here: https://github.com/thuiar/MIntRec2.0 , and among the 3 versions, I uploaded the feature and raw data(this one's raw data) because they seem to be the best way to practice on the Kaggle Notebook Environment.

The purpose of uploading this dataset is to practice multi-classification tasks using LLMs whether the large models can recognize and classify human intent properly.

Info

Source repo is google/flan-t5-large.

Usage

1. Add dataset to Kaggle notebook;
2. Import pretrained from folder;

from transformers import AutoTokenizer, AutoModel


model = AutoModel.from_pretrained('/kaggle/input/googleflan-t5-large/flan-t5-large')
tokenizer = AutoTokenizer.from_pretrained('/kaggle/input/googleflan-t5-large/flan-t5-large')

This dataset is inspired from the work of @akensert in retrieving the tiles from each image. I decided to upload my dataset version in order to train also in Kaggle notebook. I cropped the "level 1" of the original images, and each image comes with a dimension n_crops*256, 256, 3. Therefore, it is possible to retrieve the original image reshaping it to (-1, 256, 256, 3).

The Meta-Corpus of Datasets: The Reddit Dataset

The Complete Collection of Datasets Posted on Reddit

By SocialGrep [source]

About this dataset

A subreddit dataset is a collection of posts and comments made on Reddit's /r/datasets board. This dataset contains all the posts and comments made on the /r/datasets subreddit from its inception to March 1, 2022. The dataset was procured using SocialGrep. The data does not include usernames to preserve users' anonymity and to prevent targeted harassment

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In order to use this dataset, you will need to have a text editor such as Microsoft Word or LibreOffice installed on your computer. You will also need a web browser such as Google Chrome or Mozilla Firefox.

Once you have the necessary software installed, open the The Reddit Dataset folder and double-click on the the-reddit-dataset-dataset-posts.csv file to open it in your preferred text editor.

In the document, you will see a list of posts with the following information for each one: title, sentiment, score, URL, created UTC, permalink, subreddit NSFW status, and subreddit name.

You can use this information to analyze trends in data sets posted on /r/datasets over time. For example, you could calculate the average score for all posts and compare it to the average score for posts in specific subReddits. Additionally, sentiment analysis could be performed on the titles of posts to see if there is a correlation between positive/negative sentiment and upvotes/downvotes

Research Ideas

• Finding correlations between different types of datasets
• Determining which datasets are most popular on Reddit
• Analyzing the sentiments of post and comments on Reddit's /r/datasets board

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: the-reddit-dataset-dataset-comments.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | body | The body of the post. (String) | | sentiment | The sentiment of the post. (String) | | score | The score of the post. (Integer) |

File: the-reddit-dataset-dataset-posts.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | score | The score of the post. (Integer) | | domain | The domain of the post. (String) | | url | The URL of the post. (String) | | selftext | The self-text of the post. (String) | | title | The title of the post. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit SocialGrep.