Videomae Base Finetuned DogBehavior
This model is based on the VideoMae base model and was finetuned on videos of sniffer dogs detecting Parkinson's disease.
Model source
- Kind
- Other
- File size
- 329 MB
- Base model
- MCG-NJU/videomae-base
- Version
- vModel
- Updated
- Oct 24, 2026
- Source
- Hugging Face
Source description
videomae-base-finetuned-dogBehavior
This model is based on the VideoMae base model and was finetuned on videos of sniffer dogs detecting Parkinson's disease.
The dogs are trained to lie down when they detect Parkinson's disease in a sample. The training is done with a mix of negative and positive samples.
It achieves the following results on the evaluation set:
- Loss: 0.0611
- Accuracy: 0.9910
Model description
The dataset consists of video clips of 2 behavior types : lying down and exploratory sniffing the sample. The goal of the model is to detect other behavior.
Intended uses & limitations
Weights are uploaded but video clips or images are protected and cannot be shared.
Training and evaluation data
The model was trained with 25 epochs. If the dataset changes, the model is retrained for 10 epochs. The accuracy to detect the behavior of lying and sniffing is 99%. Detections that are under this confidence are considered as "other" behavior that needs further analysis in respect ot its relation to the type of sample (negative or positive).
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 adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 370
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0379 | 0.1027 | 38 | 0.0912 | 0.9820 |
| 0.2053 | 1.1027 | 76 | 0.3690 | 0.8559 |
| 0.2644 | 2.1027 | 114 | 0.0611 | 0.9910 |
| 0.1297 | 3.1027 | 152 | 0.0697 | 0.9820 |
| 0.1778 | 4.1027 | 190 | 0.1969 | 0.9640 |
| 0.1767 | 5.1027 | 228 | 0.4106 | 0.8919 |
| 0.1548 | 6.1027 | 266 | 0.2711 | 0.9369 |
| 0.127 | 7.1027 | 304 | 0.2564 | 0.9459 |
| 0.1019 | 8.1027 | 342 | 0.2746 | 0.9459 |
| 0.348 | 9.0757 | 370 | 0.2201 | 0.9550 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu130
- Datasets 4.4.2
- Tokenizers 0.22.2
Sources
basemodel: MCG-NJU/videomae-base
misc{https://doi.org/10.48550/arxiv.2203.12602, doi = {10.48550/ARXIV.2203.12602}, url = {https://arxiv.org/abs/2203.12602}, author = {Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} }
Many thanks to
Chang-Qing Gao and Ni Wang and Meimei Wang et al., "Sensitivity of Sniffer Dogs for a Diagnosis of Parkinson's Disease: A Diagnostic Accuracy Study" Aug. 2022. Lab: Chang-Qing Gao's Lab [Online].Available: {https://www.researchgate.net/publication/362936321_Sensitivity_of_Sniffer_Dogs_for_a_Diagnosis_of_Parkinson's_Disease_A_Diagnostic_Accuracy_Study}
Howest University for Applied Sciences, Creative Technologies and Artificial Intelligence
Sources
1 source
Sources
1 sourceVerified Oct 24
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- library_name: transformers tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-dogBehavior results: [] license: mit language: - en metrics: - accuracy base_model: - MCG-NJU/videomae-base pipeline_tag: video-classification --- <!-- 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. --> # videomae-base-finetuned-dogBehavior This model is based on the VideoMae base model and was finetuned on videos of sniffer dogs detecting Parkinson's disease. The dogs are trained to lie down when they detect Parkinson's disease in a sample. The training is done with a mix of negative and positive samples. It achieves the following results on the evaluation set: - Loss: 0.0611 - Accuracy: 0.9910 ## Model description The dataset consists of video clips of 2 behavior types : lying down and exploratory sniffing the sample. The goal of the model is to detect other behavior. ## Intended uses & limitations Weights are uploaded but video clips or images are protected and cannot be shared. ## Training and evaluation data The model was trained with 25 epochs. If the dataset changes, the model is retrained for 10 epochs. The accuracy to detect the behavior of lying and sniffing is 99%. Detections that are under this confidence are considered as "other" behavior that needs further analysis in respect ot its relation to the type of sample (negative or positive). ## 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 adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 370 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0379 | 0.1027 | 38 | 0.0912 | 0.9820 | | 0.2053 | 1.1027 | 76 | 0.3690 | 0.8559 | | 0.2644 | 2.1027 | 114 | 0.0611 | 0.9910 | | 0.1297 | 3.1027 | 152 | 0.0697 | 0.9820 | | 0.1778 | 4.1027 | 190 | 0.1969 | 0.9640 | | 0.1767 | 5.1027 | 228 | 0.4106 | 0.8919 | | 0.1548 | 6.1027 | 266 | 0.2711 | 0.9369 | | 0.127 | 7.1027 | 304...
Source context: 13 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo IDS75912/videomae-base-finetuned-dogBehavior
