dropoff-utcustom-train-SF-RGB-b5_1

sam1120/dropoff-utcustom-train-SF-RGB-b5_1

dropoff-utcustom-train-SF-RGB-b5_1 is a image segmentation model from sam1120. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Segmentation
Library: transformers
License: other
Model type: segformer
Access: Public
Created: Feb 12, 2024
Updated: Feb 12, 2024
SHA: 867f8166

Model card

From the Hugging Face model README

dropoff-utcustom-train-SF-RGB-b5_1

This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set:

Loss: 0.6279
Mean Iou: 0.4054
Mean Accuracy: 0.7471
Overall Accuracy: 0.8860
Accuracy Unlabeled: nan
Accuracy Dropoff: 0.5956
Accuracy Undropoff: 0.8986
Iou Unlabeled: 0.0
Iou Dropoff: 0.3318
Iou Undropoff: 0.8843

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: 2e-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Dropoff	Accuracy Undropoff	Iou Dropoff	Iou Undropoff
1.0071	5.0	10	1.0206	0.1745	0.2748	0.5034	nan	0.0255	0.5241	0.0147	0.5087
0.9688	10.0	20	0.9873	0.2140	0.3486	0.5771	nan	0.0992	0.5979	0.0582	0.5838
0.9406	15.0	30	0.9313	0.2613	0.4446	0.6655	nan	0.2038	0.6855	0.1135	0.6705
0.9278	20.0	40	0.8851	0.2930	0.5149	0.7111	nan	0.3009	0.7289	0.1648	0.7142
0.8956	25.0	50	0.8563	0.3118	0.5642	0.7358	nan	0.3770	0.7514	0.1985	0.7370
0.8674	30.0	60	0.8260	0.3303	0.6086	0.7664	nan	0.4366	0.7807	0.2246	0.7664
0.8438	35.0	70	0.8149	0.3347	0.6355	0.7671	nan	0.4921	0.7790	0.2381	0.7660
0.8309	40.0	80	0.7881	0.3459	0.6472	0.7847	nan	0.4972	0.7972	0.2539	0.7839
0.8069	45.0	90	0.7640	0.3567	0.6617	0.8041	nan	0.5063	0.8170	0.2668	0.8033
0.7779	50.0	100	0.7486	0.3637	0.6792	0.8145	nan	0.5316	0.8268	0.2778	0.8132
0.7695	55.0	110	0.7354	0.3684	0.6936	0.8214	nan	0.5542	0.8329	0.2858	0.8195
0.7568	60.0	120	0.7164	0.3757	0.7032	0.8365	nan	0.5577	0.8486	0.2924	0.8347
0.7285	65.0	130	0.6976	0.3836	0.7119	0.8484	nan	0.5630	0.8608	0.3042	0.8467
0.7217	70.0	140	0.6922	0.3857	0.7217	0.8499	nan	0.5817	0.8616	0.3091	0.8480
0.7095	75.0	150	0.6708	0.3926	0.7287	0.8624	nan	0.5828	0.8745	0.3172	0.8605
0.6944	80.0	160	0.6637	0.3951	0.7320	0.8660	nan	0.5858	0.8781	0.3212	0.8641
0.6878	85.0	170	0.6632	0.3942	0.7397	0.8673	nan	0.6005	0.8788	0.3175	0.8652
0.6868	90.0	180	0.6468	0.3998	0.7391	0.8756	nan	0.5902	0.8880	0.3257	0.8739
0.6581	95.0	190	0.6444	0.4003	0.7421	0.8776	nan	0.5942	0.8899	0.3249	0.8759
0.6587	100.0	200	0.6383	0.4026	0.7427	0.8814	nan	0.5914	0.8940	0.3281	0.8797
0.6525	105.0	210	0.6334	0.4032	0.7434	0.8825	nan	0.5918	0.8951	0.3289	0.8808
0.658	110.0	220	0.6345	0.4026	0.7451	0.8811	nan	0.5968	0.8934	0.3285	0.8793
0.6575	115.0	230	0.6300	0.4050	0.7463	0.8851	nan	0.5948	0.8977	0.3314	0.8835
0.6625	120.0	240	0.6279	0.4054	0.7471	0.8860	nan	0.5956	0.8986	0.3318	0.8843

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

Task: Image Segmentation
Library: transformers
Type: segformer
License: other
Created: Feb 12, 2024
Updated: Feb 12, 2024