Instructions to use tihon-nth/finetuned-ucf101-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tihon-nth/finetuned-ucf101-subset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="tihon-nth/finetuned-ucf101-subset")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("tihon-nth/finetuned-ucf101-subset") model = AutoModelForVideoClassification.from_pretrained("tihon-nth/finetuned-ucf101-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
finetuned-ucf101-subset
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2251
- Accuracy: 0.8516
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 370
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8372 | 0.1027 | 38 | 0.7755 | 0.1 |
| 0.4972 | 1.1027 | 76 | 0.6381 | 0.3 |
| 0.3633 | 2.1027 | 114 | 0.5018 | 0.6143 |
| 0.2332 | 3.1027 | 152 | 0.3362 | 0.7429 |
| 0.1431 | 4.1027 | 190 | 0.3253 | 0.7286 |
| 0.1360 | 5.1027 | 228 | 0.2087 | 0.8571 |
| 0.0914 | 6.1027 | 266 | 0.1770 | 0.8286 |
| 0.0544 | 7.1027 | 304 | 0.2067 | 0.7857 |
| 0.0613 | 8.1027 | 342 | 0.2690 | 0.7571 |
| 0.0387 | 9.0757 | 370 | 0.2024 | 0.8 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.1
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Base model
MCG-NJU/videomae-base