Instructions to use mwalmsley/zoobot-encoder-efficientnet_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/zoobot-encoder-efficientnet_b0 with timm:
import timm model = timm.create_model("hf-hub:mwalmsley/zoobot-encoder-efficientnet_b0", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download config.json from mwalmsley/zoobot-encoder-efficientnet_b0: direct link, hf CLI and curl.
- Browser
- Download file 575 Bytes
-
https://huggingface.co/mwalmsley/zoobot-encoder-efficientnet_b0/resolve/main/config.json
- Command line
-
hf download hf://mwalmsley/zoobot-encoder-efficientnet_b0/config.json
-
curl -L -o config.json https://huggingface.co/mwalmsley/zoobot-encoder-efficientnet_b0/resolve/main/config.json
575 Bytes
| { | |
| "architecture": "efficientnet_b0", | |
| "num_classes": 0, | |
| "num_features": 1280, | |
| "pretrained_cfg": { | |
| "tag": "ra_in1k", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "fixed_input_size": false, | |
| "interpolation": "bicubic", | |
| "crop_pct": 0.875, | |
| "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": "conv_stem", | |
| "classifier": "classifier" | |
| } | |
| } |