{
  "fetched_at": "2026-10-19T08:55:16Z",
  "source_url": "https://www.kaggle.com/competitions/garbage-guru-challenge/data",
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    {
      "name": "data-description",
      "content": "Collection of images of garbages grouped into 10 classes (metal, glass, biological, paper, battery, trash, cardboard, shoes, clothes, and plastic). This data is a subset of a [Garbage dataset](https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2). The number of files in respective classes is as follows: \n\n- **Metal**: 1000\n- **Glass**: 1000\n- **Biological**: 1000\n- **Paper**: 1000\n- **Battery**: 1000\n- **Trash**: 1000\n- **Cardboard**: 1000\n- **Shoes**: 1000\n- **Clothes**: 1000\n- **Plastic**: 1000\n\n",
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      "name": "Description",
      "content": "**The Playground, Not the Pressure Cooker**: This competition prioritizes **learning and experimentation** over cutthroat competition. We're here to provide a supportive environment where you can:\n\n- **Sharpen your AI skills**: Hone your expertise in image classification, transfer learning, and model interpretability.\n- **Get hands-on with real-world data**: Train your models on a massive dataset of diverse garbage images, reflecting the complexities of real-world waste sorting.\n- **Test and iterate**: Experiment with different approaches, analyze your results, and refine your models without the pressure of a high-stakes leaderboard.\n",
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    {
      "name": "Mission",
      "content": "Train an AI model to accurately classify different types of garbage from images. It's like playing a detective game, but with pixels instead of clues! ️‍🔎",
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    {
      "name": "rules",
      "content": "- **Fair Play First**: Cheating is a big no-no! We want to see your genuine skills and creativity in action.  Let's keep it clean and competitive!\n- **Effort Equals Expertise**: Apply yourself! This is a learning journey, so dive deep, experiment, and refine your models. Don't just throw spaghetti at the code wall. The more effort you put in, the more you'll get out of this challenge. Remember, practice makes perfect (or at least a highly accurate AI model)!\n- **Fun at the Forefront**: While we're tackling a serious issue, let's not forget to have fun! Enjoy the process of learning, share your insights with the community, and celebrate each other's accomplishments. Turning trash into treasure should be a blast!",
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    {
      "name": "abstract",
      "content": "Calling all aspiring data scientists, machine learning enthusiasts, and sustainability champions! Dive into the **Garbage Guru Challenge**, a Kaggle competition designed to level up your AI skills while tackling the critical issue of waste management.",
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    {
      "name": "Challenge",
      "content": "Craft an AI model that can:\n\n- **Ace the Sorting Exam**: Pinpoint the correct category for each garbage item with impressive precision and recall.\n- **Handle the Messy Real World**: Deal with diverse image variations like different lighting, angles, and cluttered backgrounds.\n- **No More Peek-a-Boo Trash**: Be robust to occlusions and partial visibility of waste items.\n- **Think Beyond the Training Ground**: Generalize well to unseen data and real-world scenarios, becoming a true waste-sorting master.",
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    {
      "name": "Rewards",
      "content": "- **Knowledge is Power**: Gain valuable experience and insights into AI for environmental applications.\n- **Sharpen Your Portfolio**: Showcase your skills and expertise to potential employers and collaborators.\n- **Contribute to a Greener Future**: Your learnings can pave the way for smarter waste sorting systems, promoting a more sustainable world.",
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    {
      "name": "Evaluation",
      "content": "- **Macro F1 score**: The heart of the matter! We'll measure how often your model correctly classifies each type of garbage using Macro F1-Score. See https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html.\n- **Robustness**: Can your model handle real-world challenges like blurry images and mixed waste streams? We'll test its adaptability.\n- **Interpretability**: Bonus points for models that explain their reasoning, helping us understand and improve their decision-making.\n",
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    {
      "name": "Bonus Quests ",
      "content": "- **Transfer Learning Mastery**: Utilize pre-trained image classification models and fine-tune them for garbage sorting.\n- **Data Augmentation Guru**: Unleash the power of data augmentation techniques to boost your model'sgeneralizability.\n- **Explainable AI**: Develop models that reveal their reasoning behind each classification, fostering trust and understanding.",
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    {
      "name": "Submission",
      "content": "Submission File\nFor each id in the test set, you must predict a type of class (or label). The file should contain a header and have the following format:\n\n```csv\nid,label\na762df180,0\n24c5cf439,0\n7581e896d,0\neb4b03b29,0\netc.\n```",
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    {
      "name": "Summary",
      "content": "Let's turn waste into a springboard for learning and innovation! ♻️\n\nRemember, this competition is designed to be a supportive and educational experience. We're here to cheer you on as you level up your AI skills and contribute to a more sustainable future!",
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