Instructions to use maazie/EfficientNetB0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maazie/EfficientNetB0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="maazie/EfficientNetB0") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("maazie/EfficientNetB0") model = AutoModelForImageClassification.from_pretrained("maazie/EfficientNetB0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload 2 files
Browse files- config.json +19 -16
- preprocessor_config.json +17 -17
config.json
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"architectures": [
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"depths": [
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"num_classes": 1000,
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"width_coefficient": 1.0,
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"depth_coefficient": 1.0,
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"dropout_rate": 0.2,
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"image_size": 224,
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225]
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}
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"id2label": {
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"0": "tench, Tinca tinca",
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"1": "goldfish, Carassius auratus",
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"zebra": 340,
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"zucchini, courgette": 939
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"architectures": [
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"ResNetForImageClassification"
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"depths": [
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"downsample_in_first_stage": false,
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"embedding_size": 64,
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"hidden_act": "relu",
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"hidden_sizes": [
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256,
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512,
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1024,
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"id2label": {
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"0": "tench, Tinca tinca",
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"1": "goldfish, Carassius auratus",
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"zebra": 340,
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"zucchini, courgette": 939
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"layer_type": "bottleneck",
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"model_type": "resnet",
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"num_channels": 3,
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"torch_dtype": "float32",
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"transformers_version": "4.18.0.dev0"
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}
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preprocessor_config.json
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{
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"crop_pct": 0.875,
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "
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"image_mean": [
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"image_std": [
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"resample": 3,
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"size": 224
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}
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"crop_pct": 0.875,
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "ConvNextFeatureExtractor",
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"image_mean": [
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"image_std": [
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],
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"resample": 3,
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"size": 224
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}
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