Zero-Shot Image Classification
OpenCLIP
Safetensors
fashion
retrieval
text-to-image
siglip2
e-commerce
moda
Eval Results (legacy)
Instructions to use HopitAI/moda-pro-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use HopitAI/moda-pro-lite with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:HopitAI/moda-pro-lite') tokenizer = open_clip.get_tokenizer('hf-hub:HopitAI/moda-pro-lite') - Notebooks
- Google Colab
- Kaggle
File size: 943 Bytes
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"model_cfg": {
"embed_dim": 768,
"init_logit_bias": -10,
"custom_text": true,
"vision_cfg": {
"image_size": 384,
"timm_model_name": "vit_base_patch16_siglip_384",
"timm_model_pretrained": false,
"timm_pool": "map",
"timm_proj": "none"
},
"text_cfg": {
"context_length": 64,
"vocab_size": 44646,
"hf_tokenizer_name": "HopitAI/moda-pro-lite",
"tokenizer_kwargs": {
"clean": "canonicalize"
},
"width": 768,
"heads": 12,
"layers": 12,
"no_causal_mask": true,
"proj_bias": true,
"pool_type": "last",
"norm_kwargs": {
"eps": 1e-06
},
"act_kwargs": {
"approximate": "tanh"
}
}
},
"preprocess_cfg": {
"mean": [
0.5,
0.5,
0.5
],
"std": [
0.5,
0.5,
0.5
],
"interpolation": "bicubic",
"resize_mode": "squash"
}
} |