Image Classification
OpenCLIP
Joblib
Safetensors
English
fashion
attribute-extraction
product-attributes
catalog-enrichment
e-commerce
computer-vision
multi-label-classification
siglip
moda-ner
benchmark
reproducibility
non-commercial
applicability
Eval Results (legacy)
Instructions to use HopitAI/moda-ner-v-fullbody with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use HopitAI/moda-ner-v-fullbody with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:HopitAI/moda-ner-v-fullbody') tokenizer = open_clip.get_tokenizer('hf-hub:HopitAI/moda-ner-v-fullbody') - Notebooks
- Google Colab
- Kaggle
File size: 431 Bytes
cce3d1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_provenance": "Field list is the set of packaged heads in heads/*.joblib.",
"fields": [
"glasses",
"hat",
"lower_clothing_length",
"lower_fabric",
"lower_pattern",
"neckline",
"neckwear",
"outer_clothing_cardigan",
"outer_fabric",
"outer_pattern",
"ring",
"sleeve_length",
"socks",
"upper_clothing_covering_navel",
"upper_fabric",
"upper_pattern",
"waist_accessories",
"wrist_wearing"
]
}
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