Instructions to use hf-tiny-model-private/tiny-random-ResNetBackbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ResNetBackbone with Transformers:
# Load model directly from transformers import AutoImageProcessor, ResNetBackbone processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ResNetBackbone") model = ResNetBackbone.from_pretrained("hf-tiny-model-private/tiny-random-ResNetBackbone") - Notebooks
- Google Colab
- Kaggle
File size: 720 Bytes
7c1db02 | 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 39 40 41 42 43 44 45 46 47 48 49 | {
"architectures": [
"ResNetBackbone"
],
"depths": [
1,
1,
2,
1
],
"downsample_in_first_stage": false,
"embedding_size": 64,
"embeddings_size": 10,
"hidden_act": "relu",
"hidden_sizes": [
10,
20,
30,
40
],
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"layer_type": "bottleneck",
"model_type": "resnet",
"num_channels": 3,
"out_features": [
"stage2",
"stage3",
"stage4"
],
"stage_names": [
"stem",
"stage1",
"stage2",
"stage3",
"stage4"
],
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0"
}
|