Instructions to use dsaint31/bb_mlp_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsaint31/bb_mlp_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dsaint31/bb_mlp_224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dsaint31/bb_mlp_224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,277 Bytes
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"timestamp": "20260210_120355",
"repo_id": "dsaint31/bb_mlp_224",
"revision": "main",
"tag": null,
"num_labels": 3,
"build_device": "mps",
"count": 6,
"items": [
{
"backbone": "google/vit-base-patch16-224",
"subdir": "models/google__vit-base-patch16-224",
"dirname": "google__vit-base-patch16-224"
},
{
"backbone": "microsoft/swin-tiny-patch4-window7-224",
"subdir": "models/microsoft__swin-tiny-patch4-window7-224",
"dirname": "microsoft__swin-tiny-patch4-window7-224"
},
{
"backbone": "microsoft/resnet-50",
"subdir": "models/microsoft__resnet-50",
"dirname": "microsoft__resnet-50"
},
{
"backbone": "google/efficientnet-b0",
"subdir": "models/google__efficientnet-b0",
"dirname": "google__efficientnet-b0"
},
{
"backbone": "timm/densenet121.tv_in1k",
"subdir": "models/timm__densenet121.tv_in1k",
"dirname": "timm__densenet121.tv_in1k"
},
{
"backbone": "torchvision/densenet121",
"subdir": "models/torchvision__densenet121",
"dirname": "torchvision__densenet121"
}
],
"root_code_included": true,
"root_code_files": [
"ds_proc.py",
"ds_model.py",
"ds_cfg.py",
"ds_meta.py"
]
}
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