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dacanizalesconvers/
material-surface-classifier

Name: material-surface-classifier
Availability: InStock

generated_from_trainer

timm/mobilenetv3_large_100.ra_in1k

finetune

apache-2.0

endpoints_compatible

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material-surface-classifier

This model is a fine-tuned version of timm/mobilenetv3_large_100.ra_in1k on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5208
Accuracy: 0.83
F1 Macro: 0.7133

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: 0.0003
train_batch_size: 16
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
1.3096	1.0	25	1.1979	0.54	0.4446
0.7334	2.0	50	0.6755	0.7075	0.6149
0.6720	3.0	75	0.5615	0.75	0.6328
0.5520	4.0	100	0.4911	0.7875	0.6849
0.5370	5.0	125	0.4791	0.7875	0.6668
0.4934	6.0	150	0.4929	0.825	0.7121
0.4253	7.0	175	0.4966	0.8325	0.7120
0.3215	8.0	200	0.4997	0.8175	0.7296
0.3122	9.0	225	0.4815	0.835	0.7263
0.2824	10.0	250	0.4749	0.83	0.7124
0.2727	11.0	275	0.5188	0.835	0.7255
0.1778	12.0	300	0.4973	0.8225	0.7058
0.2922	13.0	325	0.4867	0.8425	0.7355
0.2612	14.0	350	0.5655	0.85	0.7334
0.2593	15.0	375	0.5208	0.83	0.7133

Framework versions

Transformers 5.7.0
Pytorch 2.11.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2

dacanizalesconvers/material-surface-classifier

material-surface-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dacanizalesconvers/
material-surface-classifier