Instructions to use simlaharma/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simlaharma/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="simlaharma/vit-base-beans") 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("simlaharma/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("simlaharma/vit-base-beans") - Notebooks
- Google Colab
- Kaggle
Commit ·
62059cf
1
Parent(s): d3bcaee
End of training
Browse files- all_results.json +6 -6
- config.json +1 -0
- eval_results.json +3 -3
- train_results.json +3 -3
- trainer_state.json +3 -3
all_results.json
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"epoch": 5.0,
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"eval_accuracy": 0.9699248120300752,
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"eval_loss": 0.13279759883880615,
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"eval_runtime": 9.
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"eval_samples_per_second":
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"eval_steps_per_second": 0.
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"train_loss": 0.0,
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"train_runtime": 0.
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"train_samples_per_second":
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"train_steps_per_second":
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}
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"epoch": 5.0,
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"eval_accuracy": 0.9699248120300752,
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"eval_loss": 0.13279759883880615,
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"eval_runtime": 9.6147,
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"eval_samples_per_second": 13.833,
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"eval_steps_per_second": 0.936,
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"train_loss": 0.0,
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"train_runtime": 0.3337,
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"train_samples_per_second": 15491.466,
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"train_steps_per_second": 973.835
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}
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config.json
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 16,
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.26.0.dev0"
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 16,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.26.0.dev0"
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eval_results.json
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"epoch": 5.0,
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"eval_accuracy": 0.9699248120300752,
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"eval_loss": 0.13279759883880615,
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"eval_runtime": 9.
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"eval_samples_per_second":
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"eval_steps_per_second": 0.
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}
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"epoch": 5.0,
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"eval_accuracy": 0.9699248120300752,
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"eval_loss": 0.13279759883880615,
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"eval_runtime": 9.6147,
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"eval_samples_per_second": 13.833,
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"eval_steps_per_second": 0.936
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}
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train_results.json
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{
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"epoch": 5.0,
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"train_loss": 0.0,
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"train_runtime": 0.
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"train_samples_per_second":
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"train_steps_per_second":
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{
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"epoch": 5.0,
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"train_loss": 0.0,
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"train_runtime": 0.3337,
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"train_samples_per_second": 15491.466,
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"train_steps_per_second": 973.835
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}
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trainer_state.json
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"step": 325,
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"total_flos": 4.006371770595533e+17,
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"train_loss": 0.0,
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"train_runtime": 0.
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"train_samples_per_second":
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"train_steps_per_second":
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}
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],
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"max_steps": 325,
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"step": 325,
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"total_flos": 4.006371770595533e+17,
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"train_loss": 0.0,
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"train_runtime": 0.3337,
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"train_samples_per_second": 15491.466,
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"train_steps_per_second": 973.835
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}
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],
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"max_steps": 325,
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