Instructions to use hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification 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-MobileNetV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification") 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("hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
File size: 568 Bytes
d5d4394 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"architectures": [
"MobileNetV2ForImageClassification"
],
"classifier_dropout_prob": 0.1,
"depth_divisible_by": 8,
"depth_multiplier": 0.25,
"expand_ratio": 6,
"finegrained_output": true,
"first_layer_is_expansion": true,
"hidden_act": "relu6",
"image_size": 32,
"initializer_range": 0.02,
"layer_norm_eps": 0.001,
"min_depth": 8,
"model_type": "mobilenet_v2",
"num_channels": 3,
"output_stride": 32,
"semantic_loss_ignore_index": 255,
"tf_padding": true,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0"
}
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