learning_rate: 1e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
num_epochs: 40.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Node 0	Mar 100 Node 0	Map Node 1	Mar 100 Node 1
12.0634	1.0	1	34.0342	0.0388	0.1429	0.0128	-1.0	-1.0	0.083	0.0	0.2	1.0	-1.0	-1.0	1.0	0.0388	1.0	-1.0	-1.0
14.6891	2.0	2	27.5947	0.0378	0.125	0.0159	-1.0	-1.0	0.0707	0.0	0.2	1.0	-1.0	-1.0	1.0	0.0378	1.0	-1.0	-1.0
12.1372	3.0	3	24.3069	0.0538	0.2	0.0172	-1.0	-1.0	0.225	0.0	0.2	1.0	-1.0	-1.0	1.0	0.0538	1.0	-1.0	-1.0
10.1768	4.0	4	21.7997	0.0325	0.0909	0.0179	-1.0	-1.0	0.0952	0.0	0.0	1.0	-1.0	-1.0	1.0	0.0325	1.0	-1.0	-1.0
10.9674	5.0	5	20.0476	0.0507	0.1667	0.0185	-1.0	-1.0	0.1833	0.0	0.3	1.0	-1.0	-1.0	1.0	0.0507	1.0	-1.0	-1.0
8.2576	6.0	6	19.0059	0.0582	0.25	0.0185	-1.0	-1.0	0.2054	0.0	0.2	1.0	-1.0	-1.0	1.0	0.0582	1.0	-1.0	-1.0
8.2583	7.0	7	18.1974	0.0629	0.3333	0.0204	-1.0	-1.0	0.195	0.0	0.2	1.0	-1.0	-1.0	1.0	0.0629	1.0	-1.0	-1.0
7.3192	8.0	8	17.3621	0.1302	0.5	0.0435	-1.0	-1.0	0.2841	0.0	0.2	1.0	-1.0	-1.0	1.0	0.1302	1.0	-1.0	-1.0
7.0464	9.0	9	16.5316	0.2514	1.0	0.0625	-1.0	-1.0	0.3417	0.2	0.2	1.0	-1.0	-1.0	1.0	0.2514	1.0	-1.0	-1.0
6.8925	10.0	10	15.5454	0.2157	1.0	0.0196	-1.0	-1.0	0.2333	0.2	0.2	1.0	-1.0	-1.0	1.0	0.2157	1.0	-1.0	-1.0
6.9519	11.0	11	14.5089	0.225	1.0	0.0312	-1.0	-1.0	0.2667	0.2	0.2	1.0	-1.0	-1.0	1.0	0.225	1.0	-1.0	-1.0
6.2326	12.0	12	13.8831	0.3733	1.0	0.2	-1.0	-1.0	0.4114	0.3	0.6	1.0	-1.0	-1.0	1.0	0.3733	1.0	-1.0	-1.0
6.1844	13.0	13	13.3770	0.6667	1.0	0.3333	-1.0	-1.0	0.6667	0.5	1.0	1.0	-1.0	-1.0	1.0	0.6667	1.0	-1.0	-1.0
5.7356	14.0	14	12.8322	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
5.466	15.0	15	12.5074	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
5.4641	16.0	16	11.8883	0.65	1.0	0.5	-1.0	-1.0	0.65	0.3	1.0	1.0	-1.0	-1.0	1.0	0.65	1.0	-1.0	-1.0
5.3664	17.0	17	11.4002	0.65	1.0	0.5	-1.0	-1.0	0.65	0.3	1.0	1.0	-1.0	-1.0	1.0	0.65	1.0	-1.0	-1.0
4.9014	18.0	18	10.9808	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
4.7852	19.0	19	10.7451	0.65	1.0	0.5	-1.0	-1.0	0.65	0.3	1.0	1.0	-1.0	-1.0	1.0	0.65	1.0	-1.0	-1.0
4.7773	20.0	20	10.5880	0.6167	1.0	0.3333	-1.0	-1.0	0.6167	0.4	1.0	1.0	-1.0	-1.0	1.0	0.6167	1.0	-1.0	-1.0
4.6423	21.0	21	10.3569	0.75	1.0	0.5	-1.0	-1.0	0.75	0.5	1.0	1.0	-1.0	-1.0	1.0	0.75	1.0	-1.0	-1.0
4.6973	22.0	22	10.0560	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
4.5107	23.0	23	9.9010	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
4.3641	24.0	24	9.8444	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
4.3039	25.0	25	9.7284	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
4.2061	26.0	26	9.4944	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
4.1906	27.0	27	9.3099	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.9988	28.0	28	9.0558	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.9956	29.0	29	8.9970	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.9154	30.0	30	8.8224	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.8152	31.0	31	8.6420	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.7358	32.0	32	8.4847	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.7624	33.0	33	8.4232	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.6491	34.0	34	8.2848	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.5853	35.0	35	8.0934	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.5897	36.0	36	8.1184	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.4895	37.0	37	7.9605	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.415	38.0	38	7.8289	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.3717	39.0	39	7.7094	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0
3.3056	40.0	40	7.5549	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	1.0	-1.0	-1.0	1.0	1.0	1.0	-1.0	-1.0

Framework versions

Transformers 4.47.0.dev0
Pytorch 2.3.0
Datasets 3.1.0
Tokenizers 0.20.3

M4A1TasteGood/finetune-instance-segmentation-ade20k-mini-mask2former

finetune-instance-segmentation-ade20k-mini-mask2former

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

M4A1TasteGood/
finetune-instance-segmentation-ade20k-mini-mask2former