Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
English
vit
huggingpics
Eval Results (legacy)
Instructions to use IrshadG/Clothes_Pattern_Classification_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IrshadG/Clothes_Pattern_Classification_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="IrshadG/Clothes_Pattern_Classification_v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("IrshadG/Clothes_Pattern_Classification_v2") model = AutoModelForImageClassification.from_pretrained("IrshadG/Clothes_Pattern_Classification_v2") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Clothes_Pattern_Classification_v2 | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9177215099334717 | |
| license: mit | |
| language: | |
| - en | |
| # Clothes_Pattern_Classification_v2 | |
| Classification to recognize pattern types in clothes. | |
| Best results if used after cropping the image | |
| Options: | |
| 'Solid', | |
| 'Stripe', | |
| 'All over pattern', | |
| 'Melange', | |
| 'Metallic', | |
| 'Denim', | |
| 'Colour blocking', | |
| 'Dot', | |
| 'Contrast', | |
| 'Check', | |
| 'Chambray', | |
| 'Front print', | |
| 'Glittering/Metallic', | |
| 'Placement print', | |
| 'Neps', | |
| 'Embroidery', | |
| 'Lace', | |
| 'Argyle', | |
| 'Slub', | |
| 'Mesh', | |
| 'Hologram'] | |
| ## Example Images |