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	---
	license: other
	tags:
	- vision
	- image-segmentation
	- generated_from_trainer
	model-index:
	- name: dropoff-utcustom-train-SF-RGB-b0_1
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# dropoff-utcustom-train-SF-RGB-b0_1

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/dropoff-utcustom-TRAIN dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5626
	- Mean Iou: 0.4261
	- Mean Accuracy: 0.7046
	- Overall Accuracy: 0.9598
	- Accuracy Unlabeled: nan
	- Accuracy Dropoff: 0.4247
	- Accuracy Undropoff: 0.9846
	- Iou Unlabeled: 0.0
	- Iou Dropoff: 0.3192
	- Iou Undropoff: 0.9590

	## 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: 9e-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 Unlabeled \| Iou Dropoff \| Iou Undropoff \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\|
	\| 1.1029 \| 3.33 \| 10 \| 1.0852 \| 0.1637 \| 0.3955 \| 0.4522 \| nan \| 0.3333 \| 0.4577 \| 0.0 \| 0.0410 \| 0.4501 \|
	\| 1.0856 \| 6.67 \| 20 \| 1.0764 \| 0.1911 \| 0.5086 \| 0.5025 \| nan \| 0.5153 \| 0.5019 \| 0.0 \| 0.0761 \| 0.4972 \|
	\| 1.0755 \| 10.0 \| 30 \| 1.0611 \| 0.2252 \| 0.6367 \| 0.5749 \| nan \| 0.7045 \| 0.5688 \| 0.0 \| 0.1104 \| 0.5652 \|
	\| 1.0285 \| 13.33 \| 40 \| 1.0382 \| 0.2622 \| 0.7487 \| 0.6568 \| nan \| 0.8494 \| 0.6479 \| 0.0 \| 0.1420 \| 0.6445 \|
	\| 0.9935 \| 16.67 \| 50 \| 1.0151 \| 0.2893 \| 0.7814 \| 0.7201 \| nan \| 0.8486 \| 0.7141 \| 0.0 \| 0.1580 \| 0.7099 \|
	\| 0.9927 \| 20.0 \| 60 \| 0.9834 \| 0.3160 \| 0.7963 \| 0.7816 \| nan \| 0.8124 \| 0.7801 \| 0.0 \| 0.1735 \| 0.7744 \|
	\| 0.938 \| 23.33 \| 70 \| 0.9585 \| 0.3308 \| 0.8084 \| 0.8127 \| nan \| 0.8036 \| 0.8131 \| 0.0 \| 0.1860 \| 0.8065 \|
	\| 0.9169 \| 26.67 \| 80 \| 0.9376 \| 0.3457 \| 0.8169 \| 0.8376 \| nan \| 0.7943 \| 0.8396 \| 0.0 \| 0.2048 \| 0.8324 \|
	\| 0.8814 \| 30.0 \| 90 \| 0.9003 \| 0.3624 \| 0.8086 \| 0.8691 \| nan \| 0.7421 \| 0.8750 \| 0.0 \| 0.2220 \| 0.8651 \|
	\| 0.8618 \| 33.33 \| 100 \| 0.8894 \| 0.3669 \| 0.8184 \| 0.8761 \| nan \| 0.7550 \| 0.8817 \| 0.0 \| 0.2287 \| 0.8720 \|
	\| 0.8388 \| 36.67 \| 110 \| 0.8618 \| 0.3774 \| 0.8096 \| 0.8926 \| nan \| 0.7187 \| 0.9006 \| 0.0 \| 0.2431 \| 0.8892 \|
	\| 0.8878 \| 40.0 \| 120 \| 0.8269 \| 0.3929 \| 0.7937 \| 0.9140 \| nan \| 0.6618 \| 0.9257 \| 0.0 \| 0.2671 \| 0.9116 \|
	\| 0.8066 \| 43.33 \| 130 \| 0.8074 \| 0.4014 \| 0.7955 \| 0.9225 \| nan \| 0.6562 \| 0.9348 \| 0.0 \| 0.2839 \| 0.9202 \|
	\| 0.8084 \| 46.67 \| 140 \| 0.7919 \| 0.4023 \| 0.7932 \| 0.9248 \| nan \| 0.6487 \| 0.9376 \| 0.0 \| 0.2844 \| 0.9226 \|
	\| 0.7415 \| 50.0 \| 150 \| 0.7707 \| 0.4068 \| 0.7850 \| 0.9309 \| nan \| 0.6249 \| 0.9451 \| 0.0 \| 0.2913 \| 0.9290 \|
	\| 0.7508 \| 53.33 \| 160 \| 0.7326 \| 0.4154 \| 0.7660 \| 0.9415 \| nan \| 0.5735 \| 0.9585 \| 0.0 \| 0.3063 \| 0.9400 \|
	\| 0.7312 \| 56.67 \| 170 \| 0.7126 \| 0.4196 \| 0.7636 \| 0.9449 \| nan \| 0.5646 \| 0.9625 \| 0.0 \| 0.3155 \| 0.9435 \|
	\| 0.6442 \| 60.0 \| 180 \| 0.6869 \| 0.4255 \| 0.7500 \| 0.9509 \| nan \| 0.5296 \| 0.9704 \| 0.0 \| 0.3268 \| 0.9497 \|
	\| 0.6633 \| 63.33 \| 190 \| 0.6765 \| 0.4286 \| 0.7524 \| 0.9525 \| nan \| 0.5328 \| 0.9719 \| 0.0 \| 0.3343 \| 0.9513 \|
	\| 0.7247 \| 66.67 \| 200 \| 0.6557 \| 0.4307 \| 0.7335 \| 0.9568 \| nan \| 0.4886 \| 0.9785 \| 0.0 \| 0.3364 \| 0.9558 \|
	\| 0.6133 \| 70.0 \| 210 \| 0.6369 \| 0.4298 \| 0.7279 \| 0.9573 \| nan \| 0.4761 \| 0.9796 \| 0.0 \| 0.3330 \| 0.9564 \|
	\| 0.6309 \| 73.33 \| 220 \| 0.6309 \| 0.4298 \| 0.7437 \| 0.9547 \| nan \| 0.5123 \| 0.9752 \| 0.0 \| 0.3356 \| 0.9536 \|
	\| 0.6373 \| 76.67 \| 230 \| 0.6094 \| 0.4276 \| 0.7197 \| 0.9577 \| nan \| 0.4585 \| 0.9808 \| 0.0 \| 0.3262 \| 0.9568 \|
	\| 0.8436 \| 80.0 \| 240 \| 0.6195 \| 0.4341 \| 0.7438 \| 0.9569 \| nan \| 0.5101 \| 0.9776 \| 0.0 \| 0.3463 \| 0.9559 \|
	\| 0.6172 \| 83.33 \| 250 \| 0.6207 \| 0.4323 \| 0.7384 \| 0.9570 \| nan \| 0.4987 \| 0.9782 \| 0.0 \| 0.3409 \| 0.9560 \|
	\| 0.6048 \| 86.67 \| 260 \| 0.5949 \| 0.4272 \| 0.7136 \| 0.9586 \| nan \| 0.4449 \| 0.9824 \| 0.0 \| 0.3237 \| 0.9578 \|
	\| 0.7887 \| 90.0 \| 270 \| 0.6007 \| 0.4308 \| 0.7282 \| 0.9580 \| nan \| 0.4760 \| 0.9803 \| 0.0 \| 0.3353 \| 0.9571 \|
	\| 0.605 \| 93.33 \| 280 \| 0.5883 \| 0.4284 \| 0.7157 \| 0.9589 \| nan \| 0.4489 \| 0.9825 \| 0.0 \| 0.3271 \| 0.9581 \|
	\| 0.5964 \| 96.67 \| 290 \| 0.5872 \| 0.4277 \| 0.7134 \| 0.9590 \| nan \| 0.4439 \| 0.9828 \| 0.0 \| 0.3251 \| 0.9581 \|
	\| 0.6097 \| 100.0 \| 300 \| 0.5903 \| 0.4300 \| 0.7240 \| 0.9582 \| nan \| 0.4669 \| 0.9810 \| 0.0 \| 0.3325 \| 0.9573 \|
	\| 0.5886 \| 103.33 \| 310 \| 0.5710 \| 0.4250 \| 0.7035 \| 0.9594 \| nan \| 0.4227 \| 0.9843 \| 0.0 \| 0.3162 \| 0.9586 \|
	\| 0.6079 \| 106.67 \| 320 \| 0.5695 \| 0.4277 \| 0.7112 \| 0.9594 \| nan \| 0.4390 \| 0.9835 \| 0.0 \| 0.3245 \| 0.9586 \|
	\| 0.8054 \| 110.0 \| 330 \| 0.5746 \| 0.4308 \| 0.7237 \| 0.9588 \| nan \| 0.4657 \| 0.9816 \| 0.0 \| 0.3344 \| 0.9579 \|
	\| 0.5496 \| 113.33 \| 340 \| 0.5631 \| 0.4285 \| 0.7129 \| 0.9595 \| nan \| 0.4424 \| 0.9835 \| 0.0 \| 0.3269 \| 0.9587 \|
	\| 0.6271 \| 116.67 \| 350 \| 0.5761 \| 0.4302 \| 0.7214 \| 0.9589 \| nan \| 0.4608 \| 0.9819 \| 0.0 \| 0.3326 \| 0.9580 \|
	\| 0.5511 \| 120.0 \| 360 \| 0.5626 \| 0.4261 \| 0.7046 \| 0.9598 \| nan \| 0.4247 \| 0.9846 \| 0.0 \| 0.3192 \| 0.9590 \|


	### Framework versions

	- Transformers 4.30.2
	- Pytorch 2.0.1+cu117
	- Datasets 2.13.1
	- Tokenizers 0.13.3