Instructions to use kd13/Modern-SqueezeNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/Modern-SqueezeNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/Modern-SqueezeNet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/Modern-SqueezeNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete train_config.json
Browse files- train_config.json +0 -35
train_config.json
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{
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"dataset_id": "zh-plus/tiny-imagenet",
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"train_split": "train",
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"validation_split": "valid",
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"image_processor_id": "kd13/squeezenetpreprocessor0.1",
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"image_size": 64,
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"per_gpu_batch_size": 128,
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"num_workers": 2,
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"num_classes": 200,
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"fire_dropout": 0.0,
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"final_dropout": 0.5,
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"lr": 0.001,
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"betas": [
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0.9,
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0.98
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],
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"adam_eps": 1e-06,
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"weight_decay": 0.05,
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"warmup_epochs": 10,
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"epochs": 120,
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"grad_accumulation": 1,
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"max_grad_norm": 1.0,
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"label_smoothing": 0.1,
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"mixed_precision": "no",
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"seed": 42,
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"skip_val": false,
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"run_overfit_test": true,
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"overfit_batch_size": 64,
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"overfit_steps": 400,
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"overfit_lr": 0.001,
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"overfit_min_accuracy": 0.9,
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"output_dir": "/kaggle/working/output",
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"resume_from": null,
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"checkpoint_every": 20
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}
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