segmentation – AI Model by IslemTouati | AlphaNeural AI

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segmentation

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segmentation

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: -0.9095
Mean Iou: 0.2514
Mean Accuracy: 1.0
Overall Accuracy: 1.0
Per Category Iou: [0.25135050741608117]
Per Category Accuracy: [1.0]

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: 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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
-0.906	0.1905	20	-0.5145	0.2514	1.0	1.0	[0.25135050741608117]	[1.0]
-1.2793	0.3810	40	-0.7514	0.2514	1.0	1.0	[0.25135050741608117]	[1.0]
-3.2567	0.5714	60	-0.9282	0.2514	1.0	1.0	[0.25135050741608117]	[1.0]
-3.8327	0.7619	80	-0.9024	0.2514	1.0	1.0	[0.25135050741608117]	[1.0]
-2.1622	0.9524	100	-0.9095	0.2514	1.0	1.0	[0.25135050741608117]	[1.0]

Framework versions

Transformers 4.41.2
Pytorch 2.2.0
Datasets 2.20.0
Tokenizers 0.19.1