Instructions to use kd13/Modern-MobileNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/Modern-MobileNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/Modern-MobileNet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/Modern-MobileNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +5 -5
config.json
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{
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"architectures": [
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"
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],
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"auto_map": {
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"AutoConfig": "
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"AutoModelForImageClassification": "
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},
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"final_dropout": 0.5,
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"
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"id2label": {
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"0": "goldfish",
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"1": "European fire salamander",
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"slug": 198,
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"orange": 199
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},
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"model_type": "
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"num_classes": 200,
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"torch_dtype": "float32"
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}
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{
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"architectures": [
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"MobileNetForImageClassification"
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],
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"auto_map": {
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"AutoConfig": "configuration_mobilenet.MobileNetConfig",
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"AutoModelForImageClassification": "modeling_mobilenet.MobileNetForImageClassification"
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},
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"final_dropout": 0.5,
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"block_dropout": 0.0,
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"id2label": {
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"0": "goldfish",
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"1": "European fire salamander",
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"slug": 198,
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"orange": 199
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},
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"model_type": "mobilenet_custom",
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"num_classes": 200,
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"torch_dtype": "float32"
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}
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