Instructions to use electblake/clothing_type_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use electblake/clothing_type_classifier with timm:
import timm model = timm.create_model("hf_hub:electblake/clothing_type_classifier", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download config.json from electblake/clothing_type_classifier: direct link, hf CLI and curl.
- Browser
- Download file 846 Bytes
-
https://huggingface.co/electblake/clothing_type_classifier/resolve/main/config.json
- Command line
-
hf download hf://electblake/clothing_type_classifier/config.json
-
curl -L -o config.json https://huggingface.co/electblake/clothing_type_classifier/resolve/main/config.json
846 Bytes
| { | |
| "architecture": "tf_efficientnetv2_s", | |
| "num_classes": 5, | |
| "num_features": 1280, | |
| "id2label": { | |
| "0": "denim_shorts", | |
| "1": "lingerie", | |
| "2": "maid", | |
| "3": "nurse", | |
| "4": "swim_bikini" | |
| }, | |
| "label2id": { | |
| "denim_shorts": 0, | |
| "lingerie": 1, | |
| "maid": 2, | |
| "nurse": 3, | |
| "swim_bikini": 4 | |
| }, | |
| "pretrained_cfg": { | |
| "tag": "in21k_ft_in1k", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 384, | |
| 256 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 1.0, | |
| "crop_mode": "border", | |
| "mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "num_classes": 5, | |
| "pool_size": [ | |
| 12, | |
| 8 | |
| ], | |
| "first_conv": "conv_stem", | |
| "classifier": "classifier" | |
| } | |
| } | |