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finetuned-Accident-SingleLabel-Final-v3
pavitemple/finetuned-Accident-SingleLabel-Final-v3
finetuned-Accident-SingleLabel-Final-v3 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 26, 2023
- Updated
- Dec 26, 2023
- SHA
- 0dc00564
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-SingleLabel-Final-v3
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: 0.7942
- Accuracy: 0.6471
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
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 65
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.06 | 4 | 1.8584 | 0.1739 |
| No log | 1.06 | 8 | 1.6548 | 0.3478 |
| 1.6685 | 2.06 | 12 | 1.4049 | 0.5217 |
| 1.6685 | 3.06 | 16 | 1.1540 | 0.6087 |
| 1.0202 | 4.06 | 20 | 1.1246 | 0.6087 |
| 1.0202 | 5.06 | 24 | 1.0258 | 0.4348 |
| 1.0202 | 6.06 | 28 | 0.9200 | 0.5217 |
| 0.9738 | 7.06 | 32 | 0.8942 | 0.6087 |
| 0.9738 | 8.06 | 36 | 0.8556 | 0.6087 |
| 0.7315 | 9.06 | 40 | 0.9506 | 0.6087 |
| 0.7315 | 10.06 | 44 | 0.9272 | 0.6087 |
| 0.7315 | 11.06 | 48 | 0.8048 | 0.5652 |
| 0.7004 | 12.06 | 52 | 0.8537 | 0.5217 |
| 0.7004 | 13.06 | 56 | 0.8058 | 0.6087 |
| 0.7426 | 14.06 | 60 | 1.0633 | 0.6957 |
| 0.7426 | 15.06 | 64 | 0.9449 | 0.6522 |
| 0.7426 | 16.02 | 65 | 0.8110 | 0.6522 |
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 26, 2023
- Updated
- Dec 26, 2023