Instructions to use Thastp/efficientnet_b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thastp/efficientnet_b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thastp/efficientnet_b1", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Thastp/efficientnet_b1", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("Thastp/efficientnet_b1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 487 Bytes
a87fd2f | 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 | {
"auto_map": {
"AutoImageProcessor": "image_processing_efficientnet.EfficientNetImageProcessor"
},
"config": {
"crop_mode": "center",
"crop_pct": 0.9,
"input_size": [
3,
240,
240
],
"interpolation": "bicubic",
"mean": [
0.5,
0.5,
0.5
],
"std": [
0.5,
0.5,
0.5
]
},
"image_processor_type": "EfficientNetImageProcessor",
"model_name": "efficientnet_b1"
}
|