| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| tags: |
| - clip |
| - vision |
| - multimodal |
| - youtube |
| - thumbnail-analysis |
| pipeline_tag: feature-extraction |
| --- |
| |
| # TubeCLIP: AI-Driven YouTube Performance Predictor |
|
|
| **TubeCLIP is a fine-tuned variant of CLIP Large designed to analyze YouTube video thumbnails and titles to predict view performance classes.** |
|
|
| ### Why? |
| Content creators and marketers constantly rely on **guesswork, gut feelings, and tedious A/B testing** to figure out which thumbnail and title combination will drive attention. |
| **TubeCLIP solves this by replacing intuition with data-driven prediction**. By fine-tuning a CLIP model, it evaluates the complex relationship between a thumbnail's visual elements and its title to predict its potential view tier. |
|
|
| **Prediction Demo** |
| ##### (Coming Soon) |
|
|
|
|
| ### Getting it Running |
|
|
| **Prerequisites** |
|
|
| Before installing, ensure your environment meets the following requirements: |
|
|
| * Python 3.x |
| * `pytorch-lightning` |
| * `torchao==0.16.0` |
|
|
| **Installation Guide** |
|
|
| You can install the required dependencies using pip: |
|
|
| ```bash |
| pip install torch pytorch-lightning torchao==0.16.0 huggingface_hub transformers Pillow |
| |
| ``` |
|
|
| **Usage & API Examples** |
|
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| Here is a quick example of how to load the model weights from Hugging Face and run a prediction on your thumbnail and title. |
|
|
| ```python |
| import torch |
| from huggingface_hub import snapshot_download |
| from PIL import Image |
| |
| # 1. Download The Repository |
| model_path = snapshot_download(repo_id="Krudev/TubeCLIP", local_dir="/TubeCLIP" |
| ) |
| ``` |
| Now run the cli with `predict.py` |
|
|
| ```bash |
| python TubeCLIP.predict.py --model_path "/TubeCLIP/TubeCLIP.ckpt" --input_path "path/to/your/thumbnail/or/directory/of/thumbnails" --title "Your YouTube Video Title!" |
| ``` |
| --- |
|
|
| ### AI & Technical Specifics |
|
|
| **Performance Metrics & Results** |
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|
| TubeCLIP achieves **66% accuracy** in classifying video performance across three distinct view tiers: |
|
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| * **Tier 1:** 10k - 100k views |
| * **Tier 2:** 100k - 1M views |
| * **Tier 3:** 1M+ views |
|
|
| #### Evaluation Results |
|
|
| * **Test Loss:** 1.8045 |
| * **Test Accuracy:** 0.6747 (67.47%) |
|
|
| #### Classification Report |
|
|
| | Class | Precision | Recall | F1-Score | Support | |
| | :--- | :---: | :---: | :---: | :---: | |
| | **10k-100k** | 0.70 | 0.72 | 0.71 | 1,606 | |
| | **100k-1M** | 0.61 | 0.59 | 0.60 | 1,608 | |
| | **1M+** | 0.72 | 0.71 | 0.71 | 1,511 | |
| | | | | | | |
| | **Accuracy** | | | **0.67** | 4,725 | |
| | **Macro Avg** | 0.67 | 0.68 | 0.68 | 4,725 | |
| | **Weighted Avg** | 0.67 | 0.67 | 0.67 | 4,725 | |
|
|
| <img src="cfmt.png" alt="Confusion Matrix" width="600"/> |
|
|
| **Limitations & Biases** |
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|
| While the model is highly effective at distinguishing generally "good" (high potential) versus "bad" (low potential) thumbnail/title combinations, it is not an exact view-count calculator. Viewership relies on external factors (channel size, algorithmic luck, trending topics, time of day) that the model cannot see. Expect it to serve as a strong directional compass for A/B testing rather than a perfect view predictor. |
|
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| **Datasets Used** |
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| The model was trained on a custom-built, highly filtered, and strictly balanced dataset of **30,000 YouTube videos**. This curated dataset ensures the model learns pure visual-textual relationships without being overwhelmed by garbage data. |
|
|
| --- |
|
|
| ### MORE |
|
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| **License** |
|
|
| This project is open-source but restricted for commercial use. It is licensed under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. You are free to use, modify, and build upon this tool for personal and research purposes, but you may not use it for commercial gains without permission. |
|
|
| **Support & Contact** |
|
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| If you encounter bugs, have questions, or want to discuss collaboration, feel free to reach out: |
|
|
| * **Email:** krishnenduk462@gmail.com |
| * **X (Twitter):** @krishnendw |
| * **Instagram:** @contentbykrishnendu |
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|
