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finetuned-Accident-MultipleLabels-Video-subset-v2-checkpointing
pavitemple/finetuned-Accident-MultipleLabels-Video-subset-v2-checkpointing
finetuned-Accident-MultipleLabels-Video-subset-v2-checkpointing is a video classification model from pavitemple. Use it for the video classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Video Classification
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- videomae
- Access
- Public
- Created
- Dec 15, 2023
- Updated
- Nov 5, 2024
- SHA
- d603f139
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
finetuned-Accident-MultipleLabels-Video-subset-v2-checkpointing
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: 1.7371
- Accuracy: 0.3704
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 35
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.06 | 2 | 1.7265 | 0.3594 |
| No log | 1.06 | 4 | 1.6976 | 0.3906 |
| No log | 2.06 | 6 | 1.7503 | 0.3594 |
| No log | 3.06 | 8 | 1.8831 | 0.3125 |
| 1.7254 | 4.06 | 10 | 2.0285 | 0.1719 |
| 1.7254 | 5.06 | 12 | 2.0391 | 0.2812 |
| 1.7254 | 6.06 | 14 | 1.9737 | 0.3281 |
| 1.7254 | 7.06 | 16 | 1.8998 | 0.375 |
| 1.7254 | 8.06 | 18 | 1.8786 | 0.375 |
| 1.394 | 9.06 | 20 | 1.9054 | 0.3438 |
| 1.394 | 10.06 | 22 | 1.9474 | 0.3281 |
| 1.394 | 11.06 | 24 | 2.0032 | 0.3281 |
| 1.394 | 12.06 | 26 | 2.0729 | 0.3281 |
| 1.394 | 13.06 | 28 | 2.1081 | 0.3438 |
| 1.285 | 14.06 | 30 | 2.1190 | 0.3281 |
| 1.285 | 15.06 | 32 | 2.1188 | 0.3438 |
| 1.285 | 16.06 | 34 | 2.1155 | 0.3594 |
| 1.285 | 17.03 | 35 | 2.1163 | 0.3594 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Video Classification
- Library
- transformers
- Type
- videomae
- License
- cc-by-nc-4.0
- Created
- Dec 15, 2023
- Updated
- Nov 5, 2024