Instructions to use wrice/perch-v2-efficientnet-b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use wrice/perch-v2-efficientnet-b3 with timm:
import timm model = timm.create_model("hf_hub:wrice/perch-v2-efficientnet-b3", pretrained=True) - Transformers
How to use wrice/perch-v2-efficientnet-b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="wrice/perch-v2-efficientnet-b3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wrice/perch-v2-efficientnet-b3", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 896 Bytes
70c1b56 | 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 | {
"architecture": "tf_efficientnet_b3",
"num_classes": 0,
"num_features": 1536,
"model_args": {
"in_chans": 1,
"num_classes": 0,
"bn_eps": 1e-05,
"bn_momentum": 0.01
},
"pretrained_cfg": {
"tag": "ns_jft_in1k",
"custom_load": false,
"input_size": [
3,
300,
300
],
"fixed_input_size": false,
"interpolation": "bicubic",
"crop_pct": 0.904,
"crop_mode": "center",
"mean": [
0.485,
0.456,
0.406
],
"std": [
0.229,
0.224,
0.225
],
"num_classes": 1000,
"pool_size": [
10,
10
],
"first_conv": "conv_stem",
"classifier": "classifier",
"license": "apache-2.0"
},
"in_chans": 1,
"bn_eps": 1e-05,
"bn_momentum": 0.01,
"input_size": [
1,
500,
128
],
"mean": [
0.0
],
"std": [
1.0
],
"crop_pct": 1.0
} |