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
metadata
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']