resnet-101-finetuned_resnet101-adam-optimizer5e-4-autotags – AI Model by priyankloco | AlphaNeural AI

You can deploy this model and start earning money today!

priyankloco/
resnet-101-finetuned_resnet101-adam-optimizer5e-4-autotags

like

0

image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

Views

No views yet

resnet-101-finetuned_resnet101-adam-optimizer5e-4-autotags

This model is a fine-tuned version of microsoft/resnet-101 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2477
Accuracy: 0.9267

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: 0.0005
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.6033	0.99	65	2.5693	0.1381
1.5517	1.99	130	1.1376	0.6733
0.9423	2.99	195	0.6290	0.7895
0.6334	3.99	260	0.4372	0.86
0.4735	4.99	325	0.4719	0.8429
0.4573	5.99	390	0.3909	0.8590
0.3236	6.99	455	0.3507	0.8752
0.2511	7.99	520	0.2931	0.9019
0.2073	8.99	585	0.2757	0.9133
0.2174	9.99	650	0.2706	0.9114
0.1558	10.99	715	0.2654	0.9114
0.2017	11.99	780	0.2820	0.9114
0.134	12.99	845	0.2431	0.9238
0.0943	13.99	910	0.2606	0.9105
0.1396	14.99	975	0.2514	0.9229
0.1374	15.99	1040	0.2349	0.9305
0.0953	16.99	1105	0.2502	0.9210
0.0742	17.99	1170	0.2515	0.9210
0.0708	18.99	1235	0.2437	0.9257
0.0619	19.99	1300	0.2477	0.9267

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu117
Datasets 2.11.0
Tokenizers 0.13.2