# Load model directly
from transformers import AutoImageProcessor, AutoModelForVideoClassification
processor = AutoImageProcessor.from_pretrained("AidaRomano/videomae-base-finetuned")
model = AutoModelForVideoClassification.from_pretrained("AidaRomano/videomae-base-finetuned")Quick Links
videomae-base-finetuned
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9207
- Accuracy: 0.4383
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: 2236
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5277 | 0.25 | 560 | 1.0346 | 0.3915 |
| 0.9442 | 1.25 | 1120 | 0.9560 | 0.3371 |
| 0.4539 | 2.25 | 1680 | 0.9267 | 0.4097 |
| 0.4548 | 3.25 | 2236 | 0.9207 | 0.4383 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.20.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="AidaRomano/videomae-base-finetuned")