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
| { | |
| "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 | |
| } | |