Instructions to use yadvender12/AV_MAE_LAVDF_noCL_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yadvender12/AV_MAE_LAVDF_noCL_new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="yadvender12/AV_MAE_LAVDF_noCL_new")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("yadvender12/AV_MAE_LAVDF_noCL_new") model = AutoModelForVideoClassification.from_pretrained("yadvender12/AV_MAE_LAVDF_noCL_new") - Notebooks
- Google Colab
- Kaggle
AV_MAE_LAVDF_noCL_new
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4787
- Accuracy: 0.9386
- F1: 0.9383
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 19370
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7045 | 0.38 | 7354 | 0.5170 | 0.9184 | 0.9176 |
| 0.3623 | 1.38 | 14710 | 0.4973 | 0.9355 | 0.9351 |
| 0.0321 | 2.24 | 19370 | 0.4787 | 0.9386 | 0.9383 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.1
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