Instructions to use MinhLe999/3class_EfficientNetv2_ForTesting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MinhLe999/3class_EfficientNetv2_ForTesting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MinhLe999/3class_EfficientNetv2_ForTesting", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("MinhLe999/3class_EfficientNetv2_ForTesting", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 550 Bytes
e709ddb | 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 | {
"architectures": [
"UniversalVisionModel"
],
"auto_map": {
"AutoConfig": "modeling_universal_vision.UniversalVisionConfig",
"AutoModelForImageClassification": "modeling_universal_vision.UniversalVisionModel"
},
"backbone_name": "tf_efficientnetv2_s.in1k",
"dtype": "float32",
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"model_type": "universal_vision",
"transformers_version": "5.3.0",
"use_cache": false
}
|