segformer-b0-finetuned-agriculture – AI Model by imessam | AlphaNeural AI

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imessam/
segformer-b0-finetuned-agriculture

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segformer-b0-finetuned-agriculture

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3305
Mean Iou: 0.4242
Mean Accuracy: 0.5107
Overall Accuracy: 0.6733
Accuracy Unlabeled: nan
Accuracy Nutrient Deficiency: 0.6872
Accuracy Planter Skip: 0.1915
Accuracy Water: 0.8549
Accuracy Waterway: 0.1797
Accuracy Weed Cluster: 0.6404
Iou Unlabeled: 0.0
Iou Nutrient Deficiency: 0.6865
Iou Planter Skip: 0.1914
Iou Water: 0.8475
Iou Waterway: 0.1795
Iou Weed Cluster: 0.6401

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: 1e-05
train_batch_size: 2
eval_batch_size: 2
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 Unlabeled	Accuracy Nutrient Deficiency	Accuracy Planter Skip	Accuracy Water	Accuracy Waterway	Accuracy Weed Cluster	Iou Unlabeled	Iou Nutrient Deficiency	Iou Planter Skip	Iou Water	Iou Waterway	Iou Weed Cluster
0.2889	1.0	8145	0.4127	0.2578	0.3110	0.4484	nan	0.3062	0.0	0.7988	0.0007	0.4496	0.0	0.3062	0.0	0.7913	0.0007	0.4485
0.3157	2.0	16290	0.3877	0.3374	0.4070	0.5970	nan	0.5241	0.0023	0.8816	0.0303	0.5969	0.0	0.5237	0.0023	0.8715	0.0301	0.5968
0.2637	3.0	24435	0.3531	0.3717	0.4480	0.6171	nan	0.5638	0.0409	0.8804	0.1563	0.5984	0.0	0.5633	0.0409	0.8723	0.1554	0.5982
0.4715	4.0	32580	0.3337	0.3653	0.4398	0.6073	nan	0.6172	0.1068	0.8077	0.0976	0.5698	0.0	0.6164	0.1068	0.8015	0.0976	0.5696
0.0668	5.0	40725	0.3305	0.4242	0.5107	0.6733	nan	0.6872	0.1915	0.8549	0.1797	0.6404	0.0	0.6865	0.1914	0.8475	0.1795	0.6401

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2