deeplabv3-mobilevit-small_corm

This model is a fine-tuned version of apple/deeplabv3-mobilevit-small on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7777
• Mean Iou: 0.4137
• Mean Accuracy: 0.5038
• Overall Accuracy: 0.7714
• Accuracy Background: 0.9998
• Accuracy Corm: 0.0748
• Accuracy Damage: 0.4368
• Iou Background: 0.7626
• Iou Corm: 0.0734
• Iou Damage: 0.4050

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: 6e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Corm	Accuracy Damage	Iou Background	Iou Corm	Iou Damage
1.0611	0.3077	20	1.0627	0.4251	0.6398	0.6594	0.6709	0.5632	0.6853	0.6508	0.2723	0.3522
1.0077	0.6154	40	1.0227	0.5359	0.6715	0.8018	0.9058	0.4028	0.7060	0.8285	0.3049	0.4743
1.0089	0.9231	60	0.9859	0.5131	0.6138	0.8167	0.9804	0.2104	0.6506	0.8345	0.1883	0.5167
0.9397	1.2308	80	0.9637	0.4767	0.5831	0.8103	0.9867	0.0722	0.6904	0.8293	0.0701	0.5307
0.9347	1.5385	100	0.9257	0.4544	0.5545	0.7991	0.9946	0.0517	0.6172	0.8103	0.0504	0.5024
0.9007	1.8462	120	0.9054	0.4458	0.5428	0.7926	0.9968	0.0678	0.5637	0.8012	0.0658	0.4705
0.8787	2.1538	140	0.8756	0.4195	0.5140	0.7780	0.9986	0.0506	0.4927	0.7790	0.0495	0.4301
0.8757	2.4615	160	0.8501	0.4035	0.4967	0.7677	0.9994	0.0659	0.4247	0.7656	0.0645	0.3804
0.841	2.7692	180	0.8339	0.4199	0.5148	0.7799	0.9987	0.0283	0.5174	0.7791	0.0279	0.4528
0.8268	3.0769	200	0.8246	0.4358	0.5279	0.7844	0.9989	0.0809	0.5040	0.7826	0.0789	0.4460
0.8306	3.3846	220	0.8095	0.4034	0.4968	0.7690	0.9995	0.0461	0.4448	0.7653	0.0455	0.3995
0.826	3.6923	240	0.7928	0.4174	0.5078	0.7731	0.9997	0.0846	0.4391	0.7663	0.0826	0.4034
0.7873	4.0	260	0.7915	0.4150	0.5046	0.7713	0.9996	0.0842	0.4299	0.7616	0.0824	0.4009
0.8031	4.3077	280	0.7805	0.4022	0.4928	0.7648	0.9998	0.0826	0.3960	0.7557	0.0811	0.3699
0.7881	4.6154	300	0.7791	0.4352	0.5265	0.7848	0.9995	0.0659	0.5142	0.7808	0.0645	0.4604
0.7883	4.9231	320	0.7777	0.4137	0.5038	0.7714	0.9998	0.0748	0.4368	0.7626	0.0734	0.4050

Framework versions

• Transformers 4.44.1
• Pytorch 2.6.0+cpu
• Datasets 2.21.0
• Tokenizers 0.19.1