Image Feature Extraction
Transformers
Safetensors
English
multilingual
gemma4_vision
feature-extraction
vision
vit
gemma4
google
Eval Results (legacy)
Instructions to use rnagabh/gemma4-vision-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rnagabh/gemma4-vision-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="rnagabh/gemma4-vision-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rnagabh/gemma4-vision-encoder") model = AutoModel.from_pretrained("rnagabh/gemma4-vision-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 402 Bytes
3a8691c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"do_convert_rgb": true,
"do_normalize": false,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.0,
0.0,
0.0
],
"image_processor_type": "Gemma4ImageProcessor",
"image_seq_length": 280,
"image_std": [
1.0,
1.0,
1.0
],
"max_soft_tokens": 280,
"patch_size": 16,
"pooling_kernel_size": 3,
"resample": 3,
"rescale_factor": 0.00392156862745098
} |