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videomae-base-finetuned-chickenbehaviour-2

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
Inference Endpoints
Model card Files Community
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ license: cc-by-nc-4.0
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+ base_model: MCG-NJU/videomae-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: videomae-base-finetuned-chickenbehaviour-2
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # videomae-base-finetuned-chickenbehaviour-2
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+
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+ This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1357
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+ - Accuracy: 0.6697
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+ - Precision: 0.6429
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+ - Recall: 0.6697
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+ - F1: 0.6354
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 127240
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 2.035 | 0.01 | 1591 | 1.6961 | 0.5116 | 0.4264 | 0.5116 | 0.4139 |
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+ | 1.5431 | 1.01 | 3182 | 1.4395 | 0.5898 | 0.5240 | 0.5898 | 0.5167 |
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+ | 1.4118 | 2.01 | 4773 | 1.3632 | 0.6051 | 0.5553 | 0.6051 | 0.5535 |
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+ | 1.3413 | 3.01 | 6364 | 1.3312 | 0.6021 | 0.5516 | 0.6021 | 0.5384 |
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+ | 1.2969 | 4.01 | 7955 | 1.2739 | 0.6212 | 0.6122 | 0.6212 | 0.5663 |
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+ | 1.2636 | 5.01 | 9546 | 1.3212 | 0.6058 | 0.6187 | 0.6058 | 0.5430 |
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+ | 1.2231 | 6.01 | 11137 | 1.2543 | 0.6242 | 0.6461 | 0.6242 | 0.5747 |
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+ | 1.1989 | 7.01 | 12728 | 1.2378 | 0.6405 | 0.6356 | 0.6405 | 0.5869 |
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+ | 1.1566 | 8.01 | 14319 | 1.2124 | 0.6528 | 0.6199 | 0.6528 | 0.5942 |
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+ | 1.1145 | 9.01 | 15910 | 1.1803 | 0.6476 | 0.6341 | 0.6476 | 0.6052 |
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+ | 1.0567 | 10.01 | 17501 | 1.2577 | 0.6266 | 0.6279 | 0.6266 | 0.5969 |
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+ | 1.0172 | 11.01 | 19092 | 1.1961 | 0.6570 | 0.6369 | 0.6570 | 0.6083 |
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+ | 0.9817 | 12.01 | 20683 | 1.2287 | 0.6620 | 0.6499 | 0.6620 | 0.6049 |
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+ | 0.9279 | 13.01 | 22274 | 1.2358 | 0.6549 | 0.6504 | 0.6549 | 0.6213 |
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+ | 0.8913 | 14.01 | 23865 | 1.1815 | 0.6681 | 0.6325 | 0.6681 | 0.6308 |
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+ | 0.8559 | 15.01 | 25456 | 1.3212 | 0.6391 | 0.6392 | 0.6391 | 0.6037 |
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+ | 0.8083 | 16.01 | 27047 | 1.3073 | 0.6231 | 0.6251 | 0.6231 | 0.6006 |
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+ | 0.7662 | 17.01 | 28638 | 1.2982 | 0.6462 | 0.6252 | 0.6462 | 0.6214 |
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+ | 0.7363 | 18.01 | 30229 | 1.3019 | 0.6575 | 0.6428 | 0.6575 | 0.6264 |
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+ | 0.6787 | 19.01 | 31820 | 1.3867 | 0.6511 | 0.6368 | 0.6511 | 0.6230 |
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+ | 0.6433 | 20.01 | 33411 | 1.4019 | 0.6365 | 0.6375 | 0.6365 | 0.6139 |
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+ | 0.5969 | 21.01 | 35002 | 1.4419 | 0.6341 | 0.6212 | 0.6341 | 0.6104 |
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+ | 0.563 | 22.01 | 36593 | 1.4778 | 0.6509 | 0.6293 | 0.6509 | 0.6170 |
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+ | 0.5252 | 23.01 | 38184 | 1.4864 | 0.6433 | 0.6316 | 0.6433 | 0.6214 |
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+ | 0.5 | 24.01 | 39775 | 1.6704 | 0.6233 | 0.6273 | 0.6233 | 0.6023 |
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+ | 0.4622 | 25.01 | 41366 | 1.6658 | 0.6488 | 0.6260 | 0.6488 | 0.6119 |
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+ | 0.4292 | 26.01 | 42957 | 1.6428 | 0.6495 | 0.6287 | 0.6495 | 0.6243 |
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+ | 0.4044 | 27.01 | 44548 | 1.6703 | 0.6587 | 0.6311 | 0.6587 | 0.6387 |
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+ | 0.3952 | 28.01 | 46139 | 1.7576 | 0.6330 | 0.6171 | 0.6330 | 0.6123 |
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+ | 0.3681 | 29.01 | 47730 | 1.9032 | 0.6554 | 0.6349 | 0.6554 | 0.6231 |
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+ | 0.3541 | 30.01 | 49321 | 1.9508 | 0.6445 | 0.6320 | 0.6445 | 0.6207 |
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+ | 0.322 | 31.01 | 50912 | 2.1317 | 0.6226 | 0.6277 | 0.6226 | 0.6099 |
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+ | 0.3239 | 32.01 | 52503 | 1.9785 | 0.6509 | 0.6321 | 0.6509 | 0.6328 |
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+ | 0.301 | 33.01 | 54094 | 2.2050 | 0.6436 | 0.6259 | 0.6436 | 0.6097 |
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+ | 0.28 | 34.01 | 55685 | 2.2268 | 0.6320 | 0.6319 | 0.6320 | 0.6174 |
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+ | 0.2742 | 35.01 | 57276 | 2.3538 | 0.6419 | 0.6239 | 0.6419 | 0.6158 |
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+ | 0.2433 | 36.01 | 58867 | 2.3947 | 0.6478 | 0.6237 | 0.6478 | 0.6184 |
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+ | 0.2677 | 37.01 | 60458 | 2.4007 | 0.6455 | 0.6285 | 0.6455 | 0.6234 |
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+ | 0.2316 | 38.01 | 62049 | 2.5197 | 0.6297 | 0.6246 | 0.6297 | 0.6120 |
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+ | 0.2229 | 39.01 | 63640 | 2.5478 | 0.6506 | 0.6322 | 0.6506 | 0.6235 |
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+ | 0.215 | 40.01 | 65231 | 2.5168 | 0.6445 | 0.6455 | 0.6445 | 0.6209 |
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+ | 0.2032 | 41.01 | 66822 | 2.6607 | 0.6443 | 0.6304 | 0.6443 | 0.6161 |
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+ | 0.1957 | 42.01 | 68413 | 2.6434 | 0.6219 | 0.6206 | 0.6219 | 0.6059 |
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+ | 0.1839 | 43.01 | 70004 | 2.6378 | 0.6480 | 0.6182 | 0.6480 | 0.6202 |
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+ | 0.1672 | 44.01 | 71595 | 2.8355 | 0.6330 | 0.6175 | 0.6330 | 0.6095 |
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+ | 0.1554 | 45.01 | 73186 | 2.8833 | 0.6297 | 0.6180 | 0.6297 | 0.6090 |
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+ | 0.1525 | 46.01 | 74777 | 2.8732 | 0.6499 | 0.6212 | 0.6499 | 0.6247 |
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+ | 0.1443 | 47.01 | 76368 | 2.7936 | 0.6513 | 0.6240 | 0.6513 | 0.6297 |
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+ | 0.1361 | 48.01 | 77959 | 2.8815 | 0.6443 | 0.6187 | 0.6443 | 0.6230 |
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+ | 0.1351 | 49.01 | 79550 | 3.0703 | 0.6429 | 0.6244 | 0.6429 | 0.6175 |
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+ | 0.1196 | 50.01 | 81141 | 3.0275 | 0.6424 | 0.6250 | 0.6424 | 0.6190 |
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+ | 0.111 | 51.01 | 82732 | 3.1255 | 0.6419 | 0.6281 | 0.6419 | 0.6189 |
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+ | 0.1119 | 52.01 | 84323 | 3.1854 | 0.6471 | 0.6299 | 0.6471 | 0.6215 |
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+ | 0.1069 | 53.01 | 85914 | 3.2136 | 0.6384 | 0.6251 | 0.6384 | 0.6195 |
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+ | 0.093 | 54.01 | 87505 | 3.3125 | 0.6506 | 0.6145 | 0.6506 | 0.6155 |
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+ | 0.0901 | 55.01 | 89096 | 3.3028 | 0.6384 | 0.6277 | 0.6384 | 0.6217 |
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+ | 0.0776 | 56.01 | 90687 | 3.3315 | 0.6488 | 0.6272 | 0.6488 | 0.6298 |
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+ | 0.0837 | 57.01 | 92278 | 3.4385 | 0.6558 | 0.6374 | 0.6558 | 0.6242 |
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+ | 0.0701 | 58.01 | 93869 | 3.3800 | 0.6440 | 0.6321 | 0.6440 | 0.6286 |
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+ | 0.0682 | 59.01 | 95460 | 3.4473 | 0.6542 | 0.6344 | 0.6542 | 0.6262 |
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+ | 0.0763 | 60.01 | 97051 | 3.4505 | 0.6315 | 0.6149 | 0.6315 | 0.6148 |
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+ | 0.0629 | 61.01 | 98642 | 3.4402 | 0.6504 | 0.6233 | 0.6504 | 0.6253 |
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+ | 0.0552 | 62.01 | 100233 | 3.4402 | 0.6537 | 0.6324 | 0.6537 | 0.6315 |
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+ | 0.0463 | 63.01 | 101824 | 3.5300 | 0.6466 | 0.6217 | 0.6466 | 0.6217 |
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+ | 0.0471 | 64.01 | 103415 | 3.6793 | 0.6511 | 0.6346 | 0.6511 | 0.6223 |
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+ | 0.0448 | 65.01 | 105006 | 3.6850 | 0.6450 | 0.6265 | 0.6450 | 0.6170 |
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+ | 0.0362 | 66.01 | 106597 | 3.6585 | 0.6483 | 0.6265 | 0.6483 | 0.6242 |
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+ | 0.0419 | 67.01 | 108188 | 3.6285 | 0.6344 | 0.6192 | 0.6344 | 0.6169 |
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+ | 0.0309 | 68.01 | 109779 | 3.6657 | 0.6490 | 0.6264 | 0.6490 | 0.6269 |
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+ | 0.0312 | 69.01 | 111370 | 3.7123 | 0.6417 | 0.6239 | 0.6417 | 0.6205 |
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+ | 0.0315 | 70.01 | 112961 | 3.7538 | 0.6490 | 0.6224 | 0.6490 | 0.6189 |
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+ | 0.0294 | 71.01 | 114552 | 3.7064 | 0.6483 | 0.6234 | 0.6483 | 0.6237 |
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+ | 0.0282 | 72.01 | 116143 | 3.7945 | 0.6429 | 0.6247 | 0.6429 | 0.6192 |
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+ | 0.0275 | 73.01 | 117734 | 3.7550 | 0.6528 | 0.6297 | 0.6528 | 0.6272 |
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+ | 0.0319 | 74.01 | 119325 | 3.7407 | 0.6509 | 0.6289 | 0.6509 | 0.6234 |
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+ | 0.021 | 75.01 | 120916 | 3.7527 | 0.6532 | 0.6290 | 0.6532 | 0.6270 |
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+ | 0.0159 | 76.01 | 122507 | 3.7780 | 0.6516 | 0.6241 | 0.6516 | 0.6243 |
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+ | 0.0133 | 77.01 | 124098 | 3.7923 | 0.6499 | 0.6272 | 0.6499 | 0.6240 |
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+ | 0.0125 | 78.01 | 125689 | 3.8070 | 0.6504 | 0.6263 | 0.6504 | 0.6217 |
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+ | 0.0132 | 79.01 | 127240 | 3.7964 | 0.6506 | 0.6264 | 0.6506 | 0.6225 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.1
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+ - Pytorch 2.1.0
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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