Instructions to use timm/resnet50.ram_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet50.ram_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet50.ram_in1k", pretrained=True) - Transformers
How to use timm/resnet50.ram_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet50.ram_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/resnet50.ram_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from timm/resnet50.ram_in1k: direct link, hf CLI and curl.
- Browser
- Download file 722 Bytes
-
https://huggingface.co/timm/resnet50.ram_in1k/resolve/main/config.json
- Command line
-
hf download hf://timm/resnet50.ram_in1k/config.json
-
curl -L -o config.json https://huggingface.co/timm/resnet50.ram_in1k/resolve/main/config.json
722 Bytes
| { | |
| "architecture": "resnet50", | |
| "num_classes": 1000, | |
| "num_features": 2048, | |
| "pretrained_cfg": { | |
| "tag": "ram_in1k", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "test_input_size": [ | |
| 3, | |
| 288, | |
| 288 | |
| ], | |
| "fixed_input_size": false, | |
| "interpolation": "bicubic", | |
| "crop_pct": 0.875, | |
| "test_crop_pct": 0.95, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "num_classes": 1000, | |
| "pool_size": [ | |
| 7, | |
| 7 | |
| ], | |
| "first_conv": "conv1", | |
| "classifier": "fc", | |
| "origin_url": "https://github.com/huggingface/pytorch-image-models" | |
| } | |
| } |