segformer-b1-finetuned-segments-pv_v1_normalized_p100_4batch_try

This model is a fine-tuned version of nvidia/segformer-b1-finetuned-ade-512-512 on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0012
• Mean Iou: 0.9586
• Precision: 0.9787

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: 0.0004
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 40
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Precision
0.2001	0.9989	229	0.0557	0.7433	0.8099
0.0234	1.9978	458	0.0111	0.8026	0.8735
0.0109	2.9967	687	0.0069	0.8264	0.9062
0.0077	4.0	917	0.0052	0.8659	0.9087
0.0053	4.9989	1146	0.0142	0.6652	0.6845
0.0055	5.9978	1375	0.0041	0.8885	0.9319
0.0041	6.9967	1604	0.0038	0.8855	0.9516
0.0046	8.0	1834	0.0041	0.8787	0.9430
0.0041	8.9989	2063	0.0035	0.8902	0.9105
0.0035	9.9978	2292	0.0028	0.9107	0.9475
0.0034	10.9967	2521	0.0027	0.9116	0.9390
0.0038	12.0	2751	0.0031	0.9001	0.9306
0.0033	12.9989	2980	0.0025	0.9160	0.9563
0.0029	13.9978	3209	0.0026	0.9153	0.9444
0.0026	14.9967	3438	0.0023	0.9218	0.9499
0.0027	16.0	3668	0.0027	0.9086	0.9525
0.0032	16.9989	3897	0.0029	0.9076	0.9439
0.0033	17.9978	4126	0.0024	0.9192	0.9459
0.0025	18.9967	4355	0.0027	0.9108	0.9538
0.0024	20.0	4585	0.0021	0.9276	0.9559
0.0022	20.9989	4814	0.0020	0.9316	0.9649
0.0021	21.9978	5043	0.0021	0.9287	0.9571
0.0025	22.9967	5272	0.0023	0.9217	0.9511
0.0022	24.0	5502	0.0020	0.9309	0.9676
0.002	24.9989	5731	0.0018	0.9360	0.9613
0.002	25.9978	5960	0.0017	0.9394	0.9621
0.0019	26.9967	6189	0.0017	0.9403	0.9664
0.0018	28.0	6419	0.0017	0.9405	0.9566
0.0017	28.9989	6648	0.0016	0.9438	0.9695
0.0017	29.9978	6877	0.0015	0.9469	0.9755
0.002	30.9967	7106	0.0016	0.9448	0.9672
0.0018	32.0	7336	0.0015	0.9459	0.9693
0.0016	32.9989	7565	0.0014	0.9486	0.9674
0.0015	33.9978	7794	0.0013	0.9528	0.9761
0.0015	34.9967	8023	0.0013	0.9520	0.9732
0.0014	36.0	8253	0.0013	0.9541	0.9705
0.0014	36.9989	8482	0.0012	0.9563	0.9739
0.0014	37.9978	8711	0.0012	0.9575	0.9764
0.0014	38.9967	8940	0.0012	0.9581	0.9766
0.0014	39.9564	9160	0.0012	0.9586	0.9787

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1