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Timesformer Base Finetuned K400 Finetuned Yt Short Classification Sample Rate16 | ST Studio
Recursos / Timesformer Base Finetuned K400 Finetuned Yt Short Classification Sample Rate16 Timesformer Base Finetuned K400 Finetuned Yt Short Classification Sample Rate16 This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.2786 Accuracy: 0.9220 0 Precision: 0.8978 0...
This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.2786 Accuracy: 0.9220 0 Precision: 0.8978 0...
Tipo Other Modelo base facebook/timesformer-base-finetuned-k400 Versão Revision f47c279
Fonte do modelo
Tipo Other
Tamanho do arquivo 463 MB
Modelo base facebook/timesformer-base-finetuned-k400
Versão vRevision f47c279
Atualizado 26 de set. de 2026
Downloads 3
Fonte Hugging Face
timesformer-base-finetuned-k400-finetuned-yt_short_classification-sample_rate16
This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset.
It achieves the following results on the evaluation set:
Artefatos de modelo
1 artefato model.safetensors
safetensors · 463 MB · SHA-256 4bbb8f5a399c…98e9 · Hugging Face
Baixar Trechos de fonte
2 trechos
Loss: 0.2786
Accuracy: 0.9220
0 Precision: 0.8978
0 Recall: 0.9432
0 F1-score: 0.9200
0 Support: 5796.0
1 Precision: 0.9460
1 Recall: 0.9026
1 F1-score: 0.9238
1 Support: 6389.0
Accuracy F1-score: 0.9220
Macro avg Precision: 0.9219
Macro avg Recall: 0.9229
Macro avg F1-score: 0.9219
Macro avg Support: 12185.0
Weighted avg Precision: 0.9231
Weighted avg Recall: 0.9220
Weighted avg F1-score: 0.9220
Weighted avg Support: 12185.0
Model description
Intended uses & limitations
Training and evaluation data
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 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: 19800
Training results Training Loss Epoch Step Validation Loss Accuracy 0 Precision 0 Recall 0 F1-score 0 Support 1 Precision 1 Recall 1 F1-score 1 Support Accuracy F1-score Macro avg Precision Macro avg Recall Macro avg F1-score Macro avg Support Weighted avg Precision Weighted avg Recall Weighted avg F1-score Weighted avg Support 0.6099 0.0501 991 0.4427 0.7965 0.8486 0.6963 0.7650 5796.0 0.7631 0.8873 0.8205 6389.0 0.7965 0.8059 0.7918 0.7927 12185.0 0.8038 0.7965 0.7941 12185.0 0.462 1.0501 1982
Framework versions
Transformers 4.46.3
Pytorch 2.0.0+cu117
Datasets 3.1.0
Tokenizers 0.20.3
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/timesformer-base-finetuned-k400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: timesformer-base-finetuned-k400-finetuned-yt_short_classification-sample_rate16
results: []
---
<!-- 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. -->
# timesformer-base-finetuned-k400-finetuned-yt_short_classification-sample_rate16
This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2786
- Accuracy: 0.9220
- 0 Precision: 0.8978
- 0 Recall: 0.9432
- 0 F1-score: 0.9200
- 0 Support: 5796.0
- 1 Precision: 0.9460
- 1 Recall: 0.9026
- 1 F1-score: 0.9238
- 1 Support: 6389.0
- Accuracy F1-score: 0.9220
- Macro avg Precision: 0.9219
- Macro avg Recall: 0.9229
- Macro avg F1-score: 0.9219
- Macro avg Support: 12185.0
- Weighted avg Precision: 0.9231
- Weighted avg Recall: 0.9220
- Weighted avg F1-score: 0.9220
- Weighted avg Support: 12185.0
## 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: Use adamw_torch 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: 19800
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | Accuracy F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------------:|:-------------------:|:----------...
Source context: 3 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Kartikeya/timesformer-base-finetuned-k400-finetuned-yt_short_classification-sample_rate16
0.3425 2.0501 2973 0.4453 0.8008 0.7376 0.9023 0.8117 5796.0 0.8889 0.7087 0.7886 6389.0 0.8008 0.8132 0.8055 0.8002 12185.0 0.8169 0.8008 0.7996 12185.0
0.4597 3.0501 3964 0.4212 0.8135 0.8965 0.6872 0.7780 5796.0 0.7658 0.9280 0.8391 6389.0 0.8135 0.8311 0.8076 0.8086 12185.0 0.8280 0.8135 0.8101 12185.0
0.34 4.0501 4955 0.3782 0.8435 0.8672 0.7923 0.8281 5796.0 0.8253 0.8900 0.8564 6389.0 0.8435 0.8462 0.8411 0.8422 12185.0 0.8452 0.8435 0.8429 12185.0
0.2322 5.0501 5946 0.3786 0.8507 0.8105 0.8956 0.8509 5796.0 0.8953 0.8100 0.8505 6389.0 0.8507 0.8529 0.8528 0.8507 12185.0 0.8550 0.8507 0.8507 12185.0
0.3278 6.0501 6937 0.5580 0.7881 0.6988 0.9746 0.8140 5796.0 0.9642 0.6189 0.7539 6389.0 0.7881 0.8315 0.7968 0.7839 12185.0 0.8379 0.7881 0.7825 12185.0
0.3531 7.0501 7928 0.4057 0.8516 0.7948 0.9275 0.8561 5796.0 0.9225 0.7828 0.8469 6389.0 0.8516 0.8587 0.8551 0.8515 12185.0 0.8618 0.8516 0.8513 12185.0
0.2513 8.0501 8919 0.3643 0.8574 0.8021 0.9296 0.8612 5796.0 0.9254 0.7920 0.8535 6389.0 0.8574 0.8638 0.8608 0.8573 12185.0 0.8668 0.8574 0.8572 12185.0
0.2592 9.0501 9910 0.2819 0.8968 0.8775 0.9099 0.8934 5796.0 0.9155 0.8848 0.8999 6389.0 0.8968 0.8965 0.8974 0.8967 12185.0 0.8974 0.8968 0.8968 12185.0
0.405 10.0501 10901 0.3755 0.8693 0.8229 0.9241 0.8705 5796.0 0.9225 0.8195 0.8680 6389.0 0.8693 0.8727 0.8718 0.8693 12185.0 0.8751 0.8693 0.8692 12185.0
0.1971 11.0501 11892 0.3913 0.8762 0.8102 0.9662 0.8813 5796.0 0.9628 0.7946 0.8707 6389.0 0.8762 0.8865 0.8804 0.8760 12185.0 0.8902 0.8762 0.8758 12185.0
0.3499 12.0501 12883 0.2850 0.8971 0.8939 0.8892 0.8915 5796.0 0.9000 0.9042 0.9021 6389.0 0.8971 0.8969 0.8967 0.8968 12185.0 0.8971 0.8971 0.8971 12185.0
0.1551 13.0501 13874 0.2965 0.9004 0.8876 0.9051 0.8963 5796.0 0.9124 0.8961 0.9041 6389.0 0.9004 0.9000 0.9006 0.9002 12185.0 0.9006 0.9004 0.9004 12185.0
0.1379 14.0501 14865 0.3175 0.9028 0.8661 0.9412 0.9021 5796.0 0.9421 0.8681 0.9036 6389.0 0.9028 0.9041 0.9046 0.9028 12185.0 0.9060 0.9028 0.9029 12185.0
0.1979 15.0501 15856 0.5168 0.8652 0.7884 0.9793 0.8736 5796.0 0.9759 0.7616 0.8556 6389.0 0.8652 0.8822 0.8705 0.8646 12185.0 0.8867 0.8652 0.8641 12185.0
0.043 16.0501 16847 0.3269 0.9093 0.8676 0.9551 0.9093 5796.0 0.9552 0.8677 0.9094 6389.0 0.9093 0.9114 0.9114 0.9093 12185.0 0.9135 0.9093 0.9093 12185.0
0.079 17.0501 17838 0.2941 0.9156 0.8929 0.9346 0.9133 5796.0 0.9381 0.8983 0.9177 6389.0 0.9156 0.9155 0.9164 0.9155 12185.0 0.9166 0.9156 0.9156 12185.0
0.2818 18.0501 18829 0.3127 0.9137 0.8751 0.9550 0.9133 5796.0 0.9555 0.8763 0.9142 6389.0 0.9137 0.9153 0.9157 0.9137 12185.0 0.9172 0.9137 0.9138 12185.0
0.0789 19.0490 19800 0.2786 0.9220 0.8978 0.9432 0.9200 5796.0 0.9460 0.9026 0.9238 6389.0 0.9220 0.9219 0.9229 0.9219 12185.0 0.9231 0.9220 0.9220 12185.0
