Instructions to use HedronCreeper/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HedronCreeper/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HedronCreeper/results") 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("HedronCreeper/results") model = AutoModelForImageClassification.from_pretrained("HedronCreeper/results") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "MobileNetV2ForImageClassification" | |
| ], | |
| "classifier_dropout_prob": 0.2, | |
| "depth_divisible_by": 8, | |
| "depth_multiplier": 1.0, | |
| "dtype": "float32", | |
| "expand_ratio": 6, | |
| "finegrained_output": true, | |
| "first_layer_is_expansion": true, | |
| "hidden_act": "relu6", | |
| "id2label": { | |
| "0": "apple", | |
| "1": "banana" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "apple": 0, | |
| "banana": 1 | |
| }, | |
| "layer_norm_eps": 0.001, | |
| "min_depth": 8, | |
| "model_type": "mobilenet_v2", | |
| "num_channels": 3, | |
| "output_stride": 32, | |
| "problem_type": "single_label_classification", | |
| "semantic_loss_ignore_index": 255, | |
| "tf_padding": true, | |
| "transformers_version": "5.0.0" | |
| } | |