Instructions to use Shawon16/VideoMAE_Base_3_class_codeCheck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shawon16/VideoMAE_Base_3_class_codeCheck with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="Shawon16/VideoMAE_Base_3_class_codeCheck")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("Shawon16/VideoMAE_Base_3_class_codeCheck") model = AutoModelForVideoClassification.from_pretrained("Shawon16/VideoMAE_Base_3_class_codeCheck") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
confusion_matrix_test.tiff
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Git LFS Details
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Git LFS Details
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long_tail_vs_class_frequency_test.tiff
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Git LFS Details
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Git LFS Details
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overall_results_summary.csv
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mode,macro_accuracy,macro_precision,macro_recall,macro_f1,top1_accuracy,top5_accuracy,top10_accuracy
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TRAIN,1.0,1.0,1.0,1.0,1.0,1.0,1.0
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VAL,1.0,1.0,1.0,1.0,1.0,1.0,1.0
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TEST,
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mode,macro_accuracy,macro_precision,macro_recall,macro_f1,top1_accuracy,top5_accuracy,top10_accuracy
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TRAIN,1.0,1.0,1.0,1.0,1.0,1.0,1.0
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VAL,1.0,1.0,1.0,1.0,1.0,1.0,1.0
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TEST,0.9841269841269842,0.9824561403508771,0.9841269841269842,0.9828609096901779,0.9830508474576272,1.0,1.0
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per_class_results_test.csv
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class,accuracy,precision,recall,f1,macro_accuracy,macro_precision,macro_recall,macro_f1,top1_accuracy,top5_accuracy,top10_accuracy
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ac,
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aam,1.0,
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aaple,1.0,1.0,1.0,1.0,
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class,accuracy,precision,recall,f1,macro_accuracy,macro_precision,macro_recall,macro_f1,top1_accuracy,top5_accuracy,top10_accuracy
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ac,0.9523809523809523,1.0,0.9523809523809523,0.975609756097561,0.9841269841269842,0.9824561403508771,0.9841269841269842,0.9828609096901779,0.9830508474576272,1.0,1.0
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aam,1.0,0.9473684210526315,1.0,0.972972972972973,0.9841269841269842,0.9824561403508771,0.9841269841269842,0.9828609096901779,0.9830508474576272,1.0,1.0
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aaple,1.0,1.0,1.0,1.0,0.9841269841269842,0.9824561403508771,0.9841269841269842,0.9828609096901779,0.9830508474576272,1.0,1.0
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