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This model is a fine-tuned version of microsoft/resnet-18 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.8935
• Accuracy: 0.7810

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: 5e-05
• 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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
7.2836	1.0	344	7.2178	0.0038
6.6696	2.0	689	6.4685	0.0408
5.85	3.0	1034	5.3897	0.1254
5.0457	4.0	1379	4.4771	0.2143
4.3784	5.0	1723	3.6429	0.3242
3.809	6.0	2068	3.1236	0.4031
3.4229	7.0	2413	2.6388	0.4672
2.8977	8.0	2758	2.3279	0.5102
2.78	9.0	3102	2.0974	0.5682
2.4452	10.0	3447	1.8605	0.6027
2.2195	11.0	3792	1.6783	0.6312
2.1097	12.0	4137	1.6049	0.6390
1.9025	13.0	4481	1.4255	0.6912
1.7973	14.0	4826	1.3253	0.7075
1.7647	15.0	5171	1.3030	0.7032
1.6772	16.0	5516	1.1988	0.7210
1.5523	17.0	5860	1.1040	0.7395
1.4821	18.0	6205	1.0786	0.7380
1.3764	19.0	6550	1.0603	0.7471
1.2913	20.0	6895	1.0169	0.7542
1.3479	21.0	7239	0.9999	0.7563
1.3133	22.0	7584	0.9928	0.7594
1.2241	23.0	7929	0.9342	0.7649
1.1651	24.0	8274	0.9283	0.7658
1.1605	25.0	8618	0.9176	0.7720
1.0283	26.0	8963	0.8970	0.7767
1.1211	27.0	9308	0.8983	0.7754
1.1563	28.0	9653	0.8729	0.7801
1.1399	29.0	9997	0.9021	0.7732
1.1715	29.93	10320	0.8935	0.7810

Framework versions

• Transformers 4.35.2
• Pytorch 2.0.0
• Datasets 2.15.0
• Tokenizers 0.15.0