Aerial-Drone-Image-Segmentation

This model is a fine-tuned version of nvidia/mit-b0 It achieves the following results on the evaluation set:

• Loss: 0.8852
• Mean Iou: 0.2994
• Mean Accuracy: 0.3923
• Overall Accuracy: 0.7774

Model description

More information needed

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 24
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Evaluation Results

{'mean_iou': 0.27989828118195953,
 'mean_accuracy': 0.3712316062110093,
 'overall_accuracy': 0.7671712239583334,
 'per_category_iou': array([       nan, 0.8560476 , 0.32234631, 0.76880948, 0.57517691,
        0.43877125, 0.00114888, 0.14091442, 0.51807365, 0.76964765,
        0.27391949, 0.        , 0.        , 0.        , 0.        ,
        0.05778175, 0.        , 0.45566807, 0.        , 0.25864545,
        0.48767764, 0.        , 0.23313364,        nan]),
 'per_category_accuracy': array([       nan, 0.96170675, 0.43993514, 0.86977593, 0.8149788 ,
        0.49739671, 0.00114987, 0.14445379, 0.80978302, 0.88661108,
        0.46787116, 0.        , 0.        , 0.        , 0.        ,
        0.05947339, 0.        , 0.55639324, 0.        , 0.38358184,
        0.761303  , 0.        , 0.51268161,        nan])}

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy
2.7923	1.25	20	2.8338	0.0954	0.1626	0.5529
2.219	2.5	40	2.1391	0.1036	0.1666	0.5929
1.9451	3.75	60	1.7919	0.1154	0.1782	0.6129
1.7558	5.0	80	1.6767	0.1300	0.1961	0.6396
1.6381	6.25	100	1.5817	0.1383	0.2055	0.6550
1.5338	7.5	120	1.4816	0.1464	0.2140	0.6729
1.4478	8.75	140	1.4231	0.1529	0.2219	0.6823
1.361	10.0	160	1.3300	0.1637	0.2315	0.6975
1.306	11.25	180	1.3034	0.1737	0.2419	0.7060
1.2611	12.5	200	1.2692	0.1755	0.2450	0.7093
1.2317	13.75	220	1.2190	0.1821	0.2501	0.7145
1.1868	15.0	240	1.2063	0.1862	0.2539	0.7188
1.1628	16.25	260	1.1832	0.1909	0.2612	0.7234
1.1149	17.5	280	1.1368	0.2048	0.2739	0.7317
1.1009	18.75	300	1.1117	0.2232	0.2938	0.7387
1.0532	20.0	320	1.0923	0.2315	0.2997	0.7414
1.0464	21.25	340	1.0821	0.2408	0.3147	0.7480
1.0278	22.5	360	1.0541	0.2517	0.3277	0.7530
0.9945	23.75	380	1.0352	0.2612	0.3398	0.7573
0.9729	25.0	400	1.0207	0.2671	0.3511	0.7609
0.9527	26.25	420	1.0067	0.2684	0.3547	0.7609
0.9494	27.5	440	0.9870	0.2713	0.3548	0.7627
0.9287	28.75	460	0.9729	0.2745	0.3619	0.7640
0.9089	30.0	480	0.9561	0.2791	0.3640	0.7680
0.9064	31.25	500	0.9500	0.2799	0.3712	0.7672
0.8681	32.5	520	0.9397	0.2845	0.3749	0.7696
0.8677	33.75	540	0.9340	0.2835	0.3737	0.7692
0.8663	35.0	560	0.9243	0.2862	0.3755	0.7716
0.8629	36.25	580	0.9173	0.2869	0.3766	0.7719
0.8542	37.5	600	0.9112	0.2908	0.3810	0.7740
0.8391	38.75	620	0.9050	0.2904	0.3812	0.7734
0.8392	40.0	640	0.9027	0.2917	0.3818	0.7734
0.8306	41.25	660	0.8949	0.2941	0.3841	0.7755
0.8213	42.5	680	0.8936	0.2958	0.3875	0.7760
0.8406	43.75	700	0.8910	0.2964	0.3879	0.7763
0.8254	45.0	720	0.8889	0.2981	0.3897	0.7764
0.8202	46.25	740	0.8880	0.2985	0.3917	0.7767
0.8013	47.5	760	0.8891	0.2989	0.3923	0.7767
0.8188	48.75	780	0.8861	0.2994	0.3926	0.7772
0.8089	50.0	800	0.8852	0.2994	0.3923	0.7774

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2