Instructions to use thuyduong/videomae-base-finetuned-ucf101-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thuyduong/videomae-base-finetuned-ucf101-subset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="thuyduong/videomae-base-finetuned-ucf101-subset")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("thuyduong/videomae-base-finetuned-ucf101-subset") model = AutoModelForVideoClassification.from_pretrained("thuyduong/videomae-base-finetuned-ucf101-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
videomae-base-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: 0.1463
- Accuracy: 0.9548
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: 12
- eval_batch_size: 12
- 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: 300
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2901 | 0.08 | 25 | 2.1947 | 0.1286 |
| 1.5282 | 1.08 | 50 | 1.4450 | 0.5286 |
| 0.8178 | 2.08 | 75 | 0.6782 | 0.7571 |
| 0.3176 | 3.08 | 100 | 0.3843 | 0.8 |
| 0.2002 | 4.08 | 125 | 0.3049 | 0.8286 |
| 0.0912 | 5.08 | 150 | 0.1887 | 0.9571 |
| 0.1356 | 6.08 | 175 | 0.1935 | 0.9571 |
| 0.0445 | 7.08 | 200 | 0.0636 | 0.9857 |
| 0.0383 | 8.08 | 225 | 0.0824 | 0.9714 |
| 0.0139 | 9.08 | 250 | 0.1860 | 0.9143 |
| 0.0293 | 10.08 | 275 | 0.1095 | 0.9571 |
| 0.0139 | 11.08 | 300 | 0.0961 | 0.9571 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for thuyduong/videomae-base-finetuned-ucf101-subset
Base model
MCG-NJU/videomae-base