Image Feature Extraction
Transformers
Safetensors
English
gr_lite
fashion
image-retrieval
vision-transformer
dino
custom_code
Instructions to use srpone/gr-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use srpone/gr-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="srpone/gr-lite", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("srpone/gr-lite", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_attn_implementation_autoset": true, | |
| "hidden_size": 1024, | |
| "image_size": 336, | |
| "intermediate_size": 4096, | |
| "k_bias": false, | |
| "layer_norm_eps": 1e-06, | |
| "model_type": "gr_lite", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 24, | |
| "num_register_tokens": 4, | |
| "patch_size": 16, | |
| "qkv_bias": true, | |
| "transformers_version": "4.49.0", | |
| "auto_map": { | |
| "AutoConfig": "configuration_gr_lite.GRLiteConfig", | |
| "AutoModel": "modeling_gr_lite.GRLiteModel" | |
| } | |
| } |