Image Feature Extraction
Transformers
Safetensors
motif_vision
feature-extraction
motif
vision-transformer
self-supervised
video
custom_code
Instructions to use Motif-Technologies/Motif-Vision-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Motif-Technologies/Motif-Vision-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Motif-Technologies/Motif-Vision-Encoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Motif-Technologies/Motif-Vision-Encoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 900 Bytes
8c56983 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"architectures": [
"MotifVisionModel"
],
"auto_map": {
"AutoConfig": "modeling_motif_vision_encoder.MotifVisionConfig",
"AutoModel": "modeling_motif_vision_encoder.MotifVisionModel"
},
"depth": 40,
"drop_path_rate": 0.0,
"dtype": "bfloat16",
"embed_dim": 4096,
"ffn_bias": true,
"ffn_layer": "swiglu64",
"ffn_ratio": 3.0,
"gated_attention": "elementwise",
"img_size": 512,
"in_chans": 3,
"layerscale_init": 1e-05,
"mask_k_bias": true,
"model_type": "motif_vision",
"n_storage_tokens": 4,
"norm_layer": "layernormbf16",
"num_frames": 1,
"num_heads": 32,
"patch_size": 16,
"pos_embed_rope_base": 100.0,
"pos_embed_rope_rescale_coords": 2.0,
"proj_bias": true,
"qk_norm": true,
"qkv_bias": false,
"transformers_version": "5.8.1",
"tubelet_size": 2,
"untie_cls_and_patch_norms": false,
"untie_global_and_local_cls_norm": true
}
|