Instructions to use Mooshie/eva02_large_patch14_448.dbv4-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Mooshie/eva02_large_patch14_448.dbv4-full with timm:
import timm model = timm.create_model("hf_hub:Mooshie/eva02_large_patch14_448.dbv4-full", pretrained=True) - Transformers
How to use Mooshie/eva02_large_patch14_448.dbv4-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mooshie/eva02_large_patch14_448.dbv4-full") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mooshie/eva02_large_patch14_448.dbv4-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,235 Bytes
ebe5ee1 | 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | {
"pre": [
{
"background_color": "white",
"interpolation": "bilinear",
"size": [
512,
512
],
"type": "pad_to_size"
}
],
"test": [
{
"background_color": "white",
"interpolation": "bilinear",
"size": [
512,
512
],
"type": "pad_to_size"
},
{
"antialias": true,
"interpolation": "bicubic",
"max_size": null,
"size": [
448,
448
],
"type": "resize"
},
{
"size": [
448,
448
],
"type": "center_crop"
},
{
"type": "maybe_to_tensor"
},
{
"mean": [
0.48145467042922974,
0.45782750844955444,
0.40821072459220886
],
"std": [
0.2686295509338379,
0.2613025903701782,
0.27577710151672363
],
"type": "normalize"
}
],
"val": [
{
"background_color": "white",
"interpolation": "bilinear",
"size": [
512,
512
],
"type": "pad_to_size"
},
{
"antialias": true,
"interpolation": "bicubic",
"max_size": null,
"size": [
448,
448
],
"type": "resize"
},
{
"size": [
448,
448
],
"type": "center_crop"
},
{
"type": "maybe_to_tensor"
},
{
"mean": [
0.48145467042922974,
0.45782750844955444,
0.40821072459220886
],
"std": [
0.2686295509338379,
0.2613025903701782,
0.27577710151672363
],
"type": "normalize"
}
]
} |