Duplicate from bzcasper/ai-tool-pool-jewelry-vision

Co-authored-by: Robert Casper <bzcasper@users.noreply.huggingface.co>

.claude/settings.local.json

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+{
+  "permissions": {
+    "allow": [
+      "Bash(pip install:*)",
+      "Bash(huggingface-cli:*)"
+    ],
+    "deny": []
+  }
+}

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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README.dataset.txt

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+# AI Tool Pool Jewelry Vision > 2025-03-04 10:35am
+https://universe.roboflow.com/work-ypnn9/ai-tool-pool-jewelry-vision
+
+Provided by a Roboflow user
+License: CC BY 4.0
+

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- image-classification
+- zero-shot-image-classification
+- image-feature-extraction
+tags:
+- jewelry
+- fashion
+- computer-vision
+- classification
+- custom-jewelry
+pretty_name: AI Tool Pool Jewelry Vision
+size_categories:
+- 1K<n<10K
+---
+
+# AI Tool Pool Jewelry Vision Dataset
+
+## Dataset Description
+
+This dataset contains 5,130 jewelry images organized into 5 categories for computer vision tasks. The dataset was originally created and hosted on Roboflow Universe.
+
+### Categories
+- **Bracelet**: Bracelet jewelry images
+- **Earrings**: Earring jewelry images  
+- **Necklace**: Necklace jewelry images
+- **Pendant**: Pendant jewelry images
+- **Ring**: Ring jewelry images
+
+### Dataset Structure
+```
+AI-Tool-Pool-Jewelry-Vision/
+├── train/           # Training images
+├── test/            # Test images  
+├── valid/           # Validation images
+└── README.md        # This file
+```
+
+Each split contains subdirectories for each jewelry category with corresponding images.
+
+## Dataset Information
+
+- **Total Images**: 5,130
+- **Format**: CLIP format annotation
+- **License**: CC BY 4.0
+- **Source**: Roboflow Universe
+- **Original Dataset**: https://universe.roboflow.com/work-ypnn9/ai-tool-pool-jewelry-vision
+
+## Preprocessing Applied
+
+- Auto-orientation of pixel data (with EXIF-orientation stripping)
+- Auto-contrast via adaptive equalization
+
+## Data Augmentation
+
+The following augmentation was applied to create 3 versions of each source image:
+- 50% probability of horizontal flip
+- 50% probability of vertical flip  
+- Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise
+- Random rotation of between -15 and +15 degrees
+- Random brightness adjustment of between -80 and +80 percent
+
+## Use Cases
+
+This dataset is suitable for:
+- **Image Classification**: Classify jewelry types
+- **Zero-shot Classification**: Test models on jewelry categorization without training
+- **Feature Extraction**: Extract visual features from jewelry images
+- **Computer Vision Research**: General computer vision tasks with fashion/jewelry domain
+
+## Citation
+
+If you use this dataset, please cite:
+
+```
+@dataset{ai_tool_pool_jewelry_vision,
+  title={AI Tool Pool Jewelry Vision Dataset},
+  author={Roboflow User},
+  year={2025},
+  url={https://universe.roboflow.com/work-ypnn9/ai-tool-pool-jewelry-vision},
+  note={Available under CC BY 4.0 license}
+}
+```
+
+## License
+
+This dataset is available under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.# ai-tool-pool-jewelry-vision

README.roboflow.txt

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+
+AI Tool Pool Jewelry Vision - v6 2025-03-04 10:35am
+==============================
+
+This dataset was exported via roboflow.com on March 30, 2025 at 8:40 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 5130 images.
+Custom-Jewelry-Images are annotated in clip format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Auto-contrast via adaptive equalization
+
+The following augmentation was applied to create 3 versions of each source image:
+* 50% probability of horizontal flip
+* 50% probability of vertical flip
+* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise
+* Random rotation of between -15 and +15 degrees
+* Random brigthness adjustment of between -80 and +80 percent
+
+