2025-01-21-14-35-49-resnet-50

This model is a fine-tuned version of microsoft/resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1215
• Precision: 0.9786
• Recall: 0.9778
• F1: 0.9778
• Accuracy: 0.9788
• Top1 Accuracy: 0.9778
• Error Rate: 0.0212

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: 32
• eval_batch_size: 32
• seed: 3407
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Top1 Accuracy	Error Rate
1.5858	1.0	34	1.5129	0.6583	0.5407	0.5138	0.5418	0.5407	0.4582
1.3909	2.0	68	1.1807	0.7779	0.6519	0.6537	0.6522	0.6519	0.3478
1.059	3.0	102	0.7503	0.8897	0.8889	0.8867	0.8901	0.8889	0.1099
0.6942	4.0	136	0.4029	0.9427	0.9407	0.9402	0.9427	0.9407	0.0573
0.4241	5.0	170	0.2325	0.9673	0.9630	0.9624	0.9655	0.9630	0.0345
0.3235	6.0	204	0.1702	0.9673	0.9630	0.9630	0.9650	0.9630	0.0350
0.259	7.0	238	0.1359	0.9722	0.9704	0.9704	0.9719	0.9704	0.0281
0.2231	8.0	272	0.1225	0.9722	0.9704	0.9704	0.9719	0.9704	0.0281
0.2167	9.0	306	0.1253	0.9722	0.9704	0.9704	0.9719	0.9704	0.0281
0.1973	10.0	340	0.1215	0.9786	0.9778	0.9778	0.9788	0.9778	0.0212

Framework versions

• Transformers 4.45.2
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.20.3