Train – AI Model by yjwsb233 | AlphaNeural AI

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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This model is a fine-tuned version of microsoft/resnet-101 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.8647
Accuracy: 0.3282

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: 0.0002
train_batch_size: 2
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.8186	0.13	100	2.8698	0.3205
3.3401	0.26	200	2.8968	0.3026
2.8737	0.39	300	2.8947	0.3128
3.1365	0.51	400	2.8635	0.3256
2.9823	0.64	500	2.8724	0.3128
2.7439	0.77	600	2.8736	0.3333
2.7354	0.9	700	2.8708	0.3436
2.688	1.03	800	2.8709	0.3231
3.172	1.16	900	2.9082	0.2692
2.7289	1.29	1000	2.8873	0.3564
2.7369	1.41	1100	2.9032	0.3
2.879	1.54	1200	2.8807	0.3308
2.9532	1.67	1300	2.8706	0.2923
3.2004	1.8	1400	2.8598	0.2872
2.9607	1.93	1500	2.8647	0.3282

Framework versions

Transformers 4.30.1
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3