convnextv2-tiny-1k-224-finetuned-eurosat-50

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.2472
• Accuracy: 0.7763

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9434	0.97	18	1.8549	0.2847
1.7722	2.0	37	1.6757	0.3661
1.5502	2.97	55	1.4652	0.4339
1.2595	4.0	74	1.1916	0.6068
0.9304	4.97	92	1.0282	0.6576
0.7333	6.0	111	0.8574	0.7051
0.6015	6.97	129	0.8427	0.6983
0.4617	8.0	148	0.7682	0.7458
0.3162	8.97	166	0.7453	0.7559
0.2249	10.0	185	0.7475	0.7661
0.1667	10.97	203	0.7677	0.7492
0.091	12.0	222	1.0114	0.7220
0.0783	12.97	240	1.0206	0.7186
0.0613	14.0	259	0.8466	0.7492
0.0703	14.97	277	1.1067	0.7119
0.0335	16.0	296	1.0117	0.7390
0.0171	16.97	314	0.9367	0.7525
0.0253	18.0	333	1.3196	0.7153
0.0201	18.97	351	1.0530	0.7525
0.0041	20.0	370	1.0523	0.7729
0.0154	20.97	388	1.1311	0.7661
0.0025	22.0	407	1.1477	0.7729
0.0036	22.97	425	1.1309	0.7627
0.002	24.0	444	1.1399	0.7729
0.0014	24.97	462	1.1543	0.7797
0.0011	26.0	481	1.1799	0.7763
0.0011	26.97	499	1.1579	0.7661
0.0009	28.0	518	1.1907	0.7627
0.0009	28.97	536	1.1878	0.7661
0.0008	30.0	555	1.1986	0.7661
0.0008	30.97	573	1.2051	0.7661
0.0007	32.0	592	1.2073	0.7661
0.0007	32.97	610	1.2156	0.7661
0.0007	34.0	629	1.2218	0.7627
0.0007	34.97	647	1.2173	0.7661
0.0006	36.0	666	1.2217	0.7729
0.0006	36.97	684	1.2272	0.7695
0.0006	38.0	703	1.2261	0.7763
0.0006	38.97	721	1.2305	0.7763
0.0006	40.0	740	1.2325	0.7763
0.0005	40.97	758	1.2362	0.7763
0.0005	42.0	777	1.2409	0.7763
0.0005	42.97	795	1.2422	0.7763
0.0005	44.0	814	1.2429	0.7729
0.0005	44.97	832	1.2434	0.7763
0.0005	46.0	851	1.2458	0.7763
0.0005	46.97	869	1.2468	0.7763
0.0005	48.0	888	1.2471	0.7763
0.0005	48.65	900	1.2472	0.7763

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3