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
| { | |
| "timestamp": "20260210_113951", | |
| "device": "mps", | |
| "repo_id": "dsaint31/bb_mlp_224", | |
| "revision": "main", | |
| "tag": null, | |
| "num_labels": 3, | |
| "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" | |
| } | |
| ], | |
| "processor_use_fast": false | |
| } | |