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-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-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: 0.1096
+- Accuracy: 0.9733
+
+## 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: 11770
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.8643        | 0.1   | 1178  | 2.1285          | 0.5080   |
+| 0.743         | 1.1   | 2356  | 1.1040          | 0.6968   |
+| 0.2881        | 2.1   | 3534  | 0.4485          | 0.8779   |
+| 0.1943        | 3.1   | 4712  | 0.4007          | 0.8871   |
+| 0.08          | 4.1   | 5890  | 0.2446          | 0.9381   |
+| 0.0666        | 5.1   | 7068  | 0.3504          | 0.9131   |
+| 0.0825        | 6.1   | 8246  | 0.2022          | 0.9470   |
+| 0.0059        | 7.1   | 9424  | 0.1628          | 0.9607   |
+| 0.0648        | 8.1   | 10602 | 0.1387          | 0.9639   |
+| 0.002         | 9.1   | 11770 | 0.1096          | 0.9733   |
+
+
+### 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
+2025-01-24T11:05:54,bed1c574-2fac-42d1-9cef-145f6f34021f,codecarbon,15725.156623363495,0.6703727740472917,1.1239267168950007,Spain,ESP,valencia,N,,