Image Feature Extraction
Transformers
Safetensors
dinov2
dino
vision
image-embeddings
pet-recognition
Instructions to use bcd8697/trial-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bcd8697/trial-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="bcd8697/trial-model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("bcd8697/trial-model") model = AutoModel.from_pretrained("bcd8697/trial-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 385 Bytes
a7d0aee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"architectures": [
"Dinov2Model"
],
"model_type": "dinov2",
"hidden_act": "gelu",
"hidden_size": 384,
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layer_norm_eps": 1e-06,
"num_attention_heads": 6,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 14,
"qkv_bias": true,
"attention_dropout": 0.0
} |