Instructions to use tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000") 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("tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000") model = AutoModelForImageClassification.from_pretrained("tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000") - Notebooks
- Google Colab
- Kaggle
File size: 939 Bytes
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"architectures": [
"ConvNextForImageClassification"
],
"depths": [
3,
3,
27,
3
],
"drop_path_rate": 0.0,
"dtype": "float32",
"hidden_act": "gelu",
"hidden_sizes": [
128,
256,
512,
1024
],
"id2label": {
"0": "0",
"1": "1",
"2": "2",
"3": "3",
"4": "4",
"5": "5",
"6": "6",
"7": "7",
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"9": "9"
},
"image_size": 224,
"initializer_range": 0.02,
"label2id": {
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9
},
"layer_norm_eps": 1e-12,
"layer_scale_init_value": 1e-06,
"model_type": "convnext",
"num_channels": 3,
"num_stages": 4,
"out_features": [
"stage4"
],
"out_indices": [
4
],
"patch_size": 4,
"stage_names": [
"stem",
"stage1",
"stage2",
"stage3",
"stage4"
],
"transformers_version": "4.57.3"
}
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