Image Classification
Transformers
PyTorch
TensorBoard
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use douwekiela/resnet-18-finetuned-dogfood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use douwekiela/resnet-18-finetuned-dogfood with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="douwekiela/resnet-18-finetuned-dogfood") 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("douwekiela/resnet-18-finetuned-dogfood") model = AutoModelForImageClassification.from_pretrained("douwekiela/resnet-18-finetuned-dogfood", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 626 Bytes
c979108 | 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 28 29 30 31 32 33 34 35 36 37 38 | {
"_name_or_path": "microsoft/resnet-18",
"architectures": [
"ResNetForImageClassification"
],
"depths": [
2,
2,
2,
2
],
"downsample_in_first_stage": false,
"embedding_size": 64,
"hidden_act": "relu",
"hidden_sizes": [
64,
128,
256,
512
],
"id2label": {
"0": "chicken",
"1": "dog",
"2": "muffin"
},
"label2id": {
"chicken": 0,
"dog": 1,
"muffin": 2
},
"layer_type": "basic",
"model_type": "resnet",
"num_channels": 3,
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.20.1"
}
|