Model save

README.md

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+---
+license: cc-by-nc-4.0
+base_model: MCG-NJU/videomae-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: VideoMAE-URFall_UnrealFallDataset
+  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. -->
+
+# VideoMAE-URFall_UnrealFallDataset
+
+This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1220
+- Accuracy: 0.9691
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 11820
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 2.2186        | 0.1   | 1183  | 2.0737          | 0.5172   |
+| 0.8664        | 1.1   | 2366  | 0.6972          | 0.8211   |
+| 0.3324        | 2.1   | 3549  | 0.6007          | 0.8372   |
+| 0.1919        | 3.1   | 4732  | 0.5145          | 0.8651   |
+| 0.1457        | 4.1   | 5915  | 0.2822          | 0.9221   |
+| 0.0996        | 5.1   | 7098  | 0.2434          | 0.9359   |
+| 0.0159        | 6.1   | 8281  | 0.1729          | 0.9547   |
+| 0.0018        | 7.1   | 9464  | 0.1307          | 0.9649   |
+| 0.001         | 8.1   | 10647 | 0.1524          | 0.9640   |
+| 0.0013        | 9.1   | 11820 | 0.1220          | 0.9691   |
+
+
+### Framework versions
+
+- Transformers 4.38.0.dev0
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

emissions.csv

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+timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region
+2025-01-24T23:36:16,93aa3a77-4436-4b0f-bd0c-32820d086ab3,codecarbon,16062.170538425446,0.678115303660983,1.1369076078650218,Spain,ESP,valencia,N,,