Instructions to use miguelcarv/resnet-152-text-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelcarv/resnet-152-text-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="miguelcarv/resnet-152-text-detector") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("miguelcarv/resnet-152-text-detector") model = AutoModelForImageClassification.from_pretrained("miguelcarv/resnet-152-text-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from miguelcarv/resnet-152-text-detector: direct link, hf CLI and curl.
- Browser
- Download file 688 Bytes
-
https://huggingface.co/miguelcarv/resnet-152-text-detector/resolve/main/config.json
- Command line
-
hf download hf://miguelcarv/resnet-152-text-detector/config.json
-
curl -L -o config.json https://huggingface.co/miguelcarv/resnet-152-text-detector/resolve/main/config.json
688 Bytes
| { | |
| "_name_or_path": "microsoft/resnet-152", | |
| "architectures": [ | |
| "ResNetForImageClassification" | |
| ], | |
| "depths": [ | |
| 3, | |
| 8, | |
| 36, | |
| 3 | |
| ], | |
| "downsample_in_bottleneck": false, | |
| "downsample_in_first_stage": false, | |
| "embedding_size": 64, | |
| "hidden_act": "relu", | |
| "hidden_sizes": [ | |
| 256, | |
| 512, | |
| 1024, | |
| 2048 | |
| ], | |
| "layer_type": "bottleneck", | |
| "model_type": "resnet", | |
| "num_channels": 3, | |
| "out_features": [ | |
| "stage4" | |
| ], | |
| "out_indices": [ | |
| 4 | |
| ], | |
| "problem_type": "single_label_classification", | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4" | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.2" | |
| } | |