segformer-finetuned-lane-10k-steps

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-cityscapes-512-1024 on the Efferbach/lane_master dataset. It achieves the following results on the evaluation set:

• Loss: 0.0365
• Mean Iou: 0.4899
• Mean Accuracy: 0.7371
• Overall Accuracy: 0.7371
• Accuracy Background: nan
• Accuracy Left: 0.7394
• Accuracy Right: 0.7348
• Iou Background: 0.0
• Iou Left: 0.7371
• Iou Right: 0.7325

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: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: polynomial
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Left	Accuracy Right	Iou Background	Iou Left	Iou Right
0.0792	1.0	308	0.0714	0.0148	0.0229	0.0225	nan	0.0373	0.0085	0.0	0.0362	0.0083
0.0437	2.0	616	0.0502	0.1687	0.2775	0.2784	nan	0.2492	0.3058	0.0	0.2343	0.2718
0.0326	3.0	924	0.0445	0.2614	0.4441	0.4479	nan	0.3134	0.5748	0.0	0.3100	0.4742
0.0224	4.0	1232	0.0370	0.4048	0.6098	0.6100	nan	0.6043	0.6153	0.0	0.6031	0.6113
0.0184	5.0	1540	0.0346	0.3820	0.5858	0.5870	nan	0.5421	0.6295	0.0	0.5400	0.6060
0.0159	6.0	1848	0.0319	0.4367	0.6567	0.6573	nan	0.6343	0.6791	0.0	0.6341	0.6760
0.0139	7.0	2156	0.0317	0.4555	0.6855	0.6860	nan	0.6691	0.7019	0.0	0.6680	0.6986
0.0129	8.0	2464	0.0321	0.4348	0.6533	0.6535	nan	0.6479	0.6588	0.0	0.6474	0.6571
0.0122	9.0	2772	0.0275	0.4541	0.6827	0.6830	nan	0.6710	0.6943	0.0	0.6697	0.6927
0.0111	10.0	3080	0.0305	0.4609	0.6928	0.6927	nan	0.6969	0.6887	0.0	0.6963	0.6865
0.011	11.0	3388	0.0286	0.4646	0.6988	0.6991	nan	0.6890	0.7087	0.0	0.6883	0.7055
0.0103	12.0	3696	0.0298	0.4693	0.7058	0.7062	nan	0.6939	0.7177	0.0	0.6932	0.7148
0.0097	13.0	4004	0.0293	0.4717	0.7090	0.7087	nan	0.7184	0.6996	0.0	0.7176	0.6975
0.0093	14.0	4312	0.0330	0.4537	0.6835	0.6836	nan	0.6775	0.6894	0.0	0.6768	0.6843
0.009	15.0	4620	0.0331	0.4804	0.7226	0.7226	nan	0.7194	0.7257	0.0	0.7178	0.7234
0.0088	16.0	4928	0.0315	0.4890	0.7355	0.7357	nan	0.7275	0.7435	0.0	0.7259	0.7411
0.0086	17.0	5236	0.0338	0.4813	0.7234	0.7234	nan	0.7224	0.7243	0.0	0.7216	0.7223
0.0085	18.0	5544	0.0348	0.4743	0.7129	0.7126	nan	0.7225	0.7033	0.0	0.7217	0.7012
0.0083	19.0	5852	0.0357	0.4812	0.7245	0.7244	nan	0.7281	0.7210	0.0	0.7254	0.7183
0.0081	20.0	6160	0.0334	0.4829	0.7271	0.7269	nan	0.7337	0.7205	0.0	0.7305	0.7182
0.0079	21.0	6468	0.0359	0.4773	0.7177	0.7177	nan	0.7184	0.7170	0.0	0.7174	0.7146
0.0077	22.0	6776	0.0351	0.4874	0.7332	0.7329	nan	0.7440	0.7223	0.0	0.7432	0.7190
0.0075	23.0	7084	0.0344	0.4855	0.7296	0.7292	nan	0.7437	0.7156	0.0	0.7425	0.7141
0.0077	24.0	7392	0.0362	0.4799	0.7216	0.7216	nan	0.7236	0.7196	0.0	0.7223	0.7174
0.0071	25.0	7700	0.0391	0.4775	0.7179	0.7180	nan	0.7173	0.7186	0.0	0.7161	0.7163
0.0077	26.0	8008	0.0339	0.4895	0.7367	0.7366	nan	0.7405	0.7329	0.0	0.7388	0.7297
0.0069	27.0	8316	0.0344	0.4858	0.7305	0.7305	nan	0.7291	0.7318	0.0	0.7278	0.7297
0.0069	28.0	8624	0.0361	0.4844	0.7283	0.7282	nan	0.7324	0.7243	0.0	0.7309	0.7221
0.007	29.0	8932	0.0371	0.4837	0.7273	0.7270	nan	0.7360	0.7186	0.0	0.7345	0.7166
0.007	30.0	9240	0.0366	0.4854	0.7305	0.7303	nan	0.7379	0.7231	0.0	0.7353	0.7208
0.0067	31.0	9548	0.0367	0.4866	0.7322	0.7321	nan	0.7357	0.7286	0.0	0.7335	0.7263
0.0068	32.0	9856	0.0364	0.4883	0.7348	0.7347	nan	0.7377	0.7318	0.0	0.7355	0.7295
0.0067	32.47	10000	0.0365	0.4899	0.7371	0.7371	nan	0.7394	0.7348	0.0	0.7371	0.7325

Framework versions

• Transformers 4.28.0.dev0
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3