Instructions to use Mamba824/custom_resnet50d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mamba824/custom_resnet50d with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mamba824/custom_resnet50d", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Mamba824/custom_resnet50d", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("Mamba824/custom_resnet50d", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 545 Bytes
24e9baf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"architectures": [
"ResnetModelForImageClassification"
],
"auto_map": {
"AutoConfig": "configuration_resnet.ResnetConfig",
"AutoModelForImageClassification": "modeling_resnet.ResnetModelForImageClassification"
},
"avg_down": true,
"base_width": 64,
"block_type": "bottleneck",
"cardinality": 1,
"dtype": "float32",
"input_channels": 3,
"layers": [
3,
4,
6,
3
],
"model_type": "resnet",
"num_classes": 1000,
"stem_type": "deep",
"stem_width": 32,
"transformers_version": "4.57.6"
}
|