nateraw

--- tags: - image-classification - pytorch datasets: - nateraw/fairface ---

1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Model Examination 7. Environmental Impact 8. Technical Specifications 9. Citation 10. Glossary 11. More Information 12. Model Card Authors 13. Model Card Contact 14. How To Get Started With the Model - Developed by: @nateraw - Shared by [optional]: [More Information Needed] - Model type: fine-tuned - Language(s) (NLP): en - License: mit - Related Models [optional]: [More Information Needed] - Parent Model [optional]: MCG-NJU/videomae-base - Resources for more information: [More Information Needed] This model can be used for Video Action Recognition Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations. We sampled clips from the videos of 64 frames, then took a uniform sample of those frames to get 16 frame inputs for the model. During training, we used PyTorchVideo's `MixVideo` to apply mixup/cutmix. We only trained/evaluated one fold from the UCF101 annotations. Unlike in the VideoMAE paper, we did not perform inference over multiple crops/segments of validation videos, so the results are likely slightly lower than what you would get if you did that too. - Eval Accuracy: 0.758209764957428 - Eval Accuracy Top 5: 0.8983050584793091 Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]