Image Classification
PyTorch
Safetensors
Transformers
English
resnet10
feature-extraction
jax-conversion
resnet
hil-serl
Lerobot
vision
custom_code
Instructions to use lilkm/resnet10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilkm/resnet10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lilkm/resnet10", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lilkm/resnet10", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 443 Bytes
8215eed 5e193f9 8215eed 5e193f9 8215eed 5e193f9 8215eed 39312b0 8215eed | 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 | {
"architectures": [
"ResNet10"
],
"auto_map": {
"AutoConfig": "configuration_resnet.ResNet10Config",
"AutoModel": "modeling_resnet.ResNet10"
},
"depths": [
1,
1,
1,
1
],
"dtype": "float32",
"embedding_size": 64,
"hidden_act": "relu",
"hidden_sizes": [
64,
128,
256,
512
],
"model_type": "resnet10",
"num_channels": 3,
"pooler": null,
"transformers_version": "5.3.0"
}
|