End of training

README.md

@@ -0,0 +1,101 @@
+---
+license: other
+base_model: nvidia/mit-b0
+tags:
+- generated_from_trainer
+model-index:
+- name: segformer-webots-grasp
+  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. -->
+
+# segformer-webots-grasp
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 4.4405
+- Mean Iou: 0.0802
+- Mean Accuracy: 0.8142
+- Overall Accuracy: 0.8142
+- Per Category Iou: [0.8018109121927621, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]
+- Per Category Accuracy: [0.8141941463133944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
+
+## 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                                                                             |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
+| 5.4277        | 0.03  | 1    | 5.4055          | 0.0001   | 0.0011        | 0.0011           | [0.0010665493208110168, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0] | [0.001069721346006343, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
+| 5.3872        | 0.05  | 2    | 5.3965          | 0.0004   | 0.0053        | 0.0053           | [0.005245532234607124, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]  | [0.005252500148444879, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
+| 5.3659        | 0.07  | 3    | 5.3862          | 0.0013   | 0.0160        | 0.0160           | [0.01595653583377678, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]   | [0.015969302211124733, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
+| 5.2685        | 0.1   | 4    | 5.3741          | 0.0037   | 0.0445        | 0.0445           | [0.04445613702342968, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]   | [0.044479595103583916, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
+| 5.2464        | 0.12  | 5    | 5.3603          | 0.0113   | 0.1359        | 0.1359           | [0.13585817440138379, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]   | [0.1359222528362154, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 5.1881        | 0.15  | 6    | 5.3461          | 0.0240   | 0.2880        | 0.2880           | [0.287886510883373, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]     | [0.2879980631769163, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 5.1556        | 0.17  | 7    | 5.3322          | 0.0329   | 0.3949        | 0.3949           | [0.3948099641784779, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]    | [0.3949236748463366, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 5.0952        | 0.2   | 8    | 5.3175          | 0.0400   | 0.4807        | 0.4807           | [0.4805425893875233, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]    | [0.4807401186212317, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 5.0421        | 0.23  | 9    | 5.3015          | 0.0606   | 0.5455        | 0.5455           | [0.5450016850554845, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.5454547402275828, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 5.0168        | 0.25  | 10   | 5.2843          | 0.0659   | 0.5936        | 0.5936           | [0.5926648159557198, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.5935808761369806, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.9417        | 0.28  | 11   | 5.2641          | 0.0575   | 0.6341        | 0.6341           | [0.6324597013631272, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.6340619477314868, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.8962        | 0.3   | 12   | 5.2394          | 0.0606   | 0.6696        | 0.6696           | [0.6670764824873869, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.6695910817989438, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.9963        | 0.33  | 13   | 5.2132          | 0.0574   | 0.6918        | 0.6918           | [0.6887950316615776, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.6918253700562499, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.9677        | 0.35  | 14   | 5.1887          | 0.0703   | 0.7061        | 0.7061           | [0.7026749604186757, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7060962792061474, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.921         | 0.38  | 15   | 5.1585          | 0.0799   | 0.7228        | 0.7228           | [0.7188799212945968, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7228307612069762, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.9849        | 0.4   | 16   | 5.1298          | 0.0808   | 0.7311        | 0.7311           | [0.7268635588232967, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7311038850932571, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.8045        | 0.42  | 17   | 5.0921          | 0.0823   | 0.7458        | 0.7458           | [0.7409411768283134, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7458280867505752, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.6795        | 0.45  | 18   | 5.0554          | 0.0835   | 0.7575        | 0.7575           | [0.7518766884727517, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7574526981157601, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.7354        | 0.47  | 19   | 5.0164          | 0.0848   | 0.7698        | 0.7698           | [0.7632263888994257, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7697950481236874, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.64          | 0.5   | 20   | 4.9783          | 0.0771   | 0.7779        | 0.7779           | [0.7705429510590344, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7778955474494109, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.6682        | 0.53  | 21   | 4.9336          | 0.0777   | 0.7858        | 0.7858           | [0.7773627351835143, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7858173007762596, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.6887        | 0.55  | 22   | 4.8904          | 0.0782   | 0.7914        | 0.7914           | [0.7821032704184515, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.791440760086753, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]    |
+| 4.5486        | 0.57  | 23   | 4.8541          | 0.0785   | 0.7943        | 0.7943           | [0.7845670293951555, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7943371186267411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.6823        | 0.6   | 24   | 4.8202          | 0.0787   | 0.7972        | 0.7972           | [0.786876415221171, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]     | [0.797181751012945, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]    |
+| 4.5797        | 0.62  | 25   | 4.7832          | 0.0789   | 0.8001        | 0.8001           | [0.7893493389876929, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8000900463576524, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.5976        | 0.65  | 26   | 4.7564          | 0.0789   | 0.7999        | 0.7999           | [0.7892736529850544, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7998681442186382, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.5417        | 0.68  | 27   | 4.7152          | 0.0793   | 0.8040        | 0.8040           | [0.7927347074267436, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8040157247507503, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.608         | 0.7   | 28   | 4.6820          | 0.0794   | 0.8053        | 0.8053           | [0.7938510702940416, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8053333643486208, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4955        | 0.72  | 29   | 4.6511          | 0.0795   | 0.8062        | 0.8062           | [0.7946513690612669, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8061980175109864, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.5359        | 0.75  | 30   | 4.6301          | 0.0794   | 0.8057        | 0.8057           | [0.7942641352298992, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8056544337883806, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4116        | 0.78  | 31   | 4.5990          | 0.0797   | 0.8094        | 0.8094           | [0.7974774354228353, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8094237094324631, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4995        | 0.8   | 32   | 4.5779          | 0.0798   | 0.8098        | 0.8098           | [0.7978091719139048, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8097701828412411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.5087        | 0.82  | 33   | 4.5585          | 0.0799   | 0.8112        | 0.8112           | [0.7989926828268309, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8112010690479878, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4534        | 0.85  | 34   | 4.5346          | 0.0799   | 0.8116        | 0.8116           | [0.7994616833790221, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8116476279732591, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4347        | 0.88  | 35   | 4.5080          | 0.0800   | 0.8121        | 0.8121           | [0.7999173944895492, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8121012265525958, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4389        | 0.9   | 36   | 4.4889          | 0.0800   | 0.8119        | 0.8119           | [0.7998204784246967, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8119105437490014, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.428         | 0.93  | 37   | 4.4843          | 0.0799   | 0.8105        | 0.8105           | [0.7987525641385264, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8105448508603376, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.5333        | 0.95  | 38   | 4.4847          | 0.0798   | 0.8094        | 0.8094           | [0.7978741861189972, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8093536189637262, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4458        | 0.97  | 39   | 4.4634          | 0.0798   | 0.8098        | 0.8098           | [0.7981748026222505, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8097799771425493, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+| 4.4749        | 1.0   | 40   | 4.4405          | 0.0802   | 0.8142        | 0.8142           | [0.8018109121927621, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8141941463133944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

@@ -0,0 +1,524 @@
+{
+  "_name_or_path": "nvidia/mit-b0",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 256,
+  "depths": [
+    2,
+    2,
+    2,
+    2
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    32,
+    64,
+    160,
+    256
+  ],
+  "id2label": {
+    "0": "dummy0",
+    "1": "dummy1",
+    "2": "dummy2",
+    "3": "dummy3",
+    "4": "dummy4",
+    "5": "dummy5",
+    "6": "dummy6",
+    "7": "dummy7",
+    "8": "dummy8",
+    "9": "dummy9",
+    "10": "dummy10",
+    "11": "dummy11",
+    "12": "dummy12",
+    "13": "dummy13",
+    "14": "dummy14",
+    "15": "dummy15",
+    "16": "dummy16",
+    "17": "dummy17",
+    "18": "dummy18",
+    "19": "dummy19",
+    "20": "dummy20",
+    "21": "dummy21",
+    "22": "dummy22",
+    "23": "dummy23",
+    "24": "dummy24",
+    "25": "dummy25",
+    "26": "dummy26",
+    "27": "dummy27",
+    "28": "dummy28",
+    "29": "dummy29",
+    "30": "dummy30",
+    "31": "dummy31",
+    "32": "dummy32",
+    "33": "dummy33",
+    "34": "dummy34",
+    "35": "dummy35",
+    "36": "dummy36",
+    "37": "dummy37",
+    "38": "dummy38",
+    "39": "dummy39",
+    "40": "dummy40",
+    "41": "dummy41",
+    "42": "dummy42",
+    "43": "dummy43",
+    "44": "dummy44",
+    "45": "dummy45",
+    "46": "dummy46",
+    "47": "dummy47",
+    "48": "dummy48",
+    "49": "dummy49",
+    "50": "dummy50",
+    "51": "dummy51",
+    "52": "dummy52",
+    "53": "dummy53",
+    "54": "dummy54",
+    "55": "dummy55",
+    "56": "dummy56",
+    "57": "dummy57",
+    "58": "dummy58",
+    "59": "dummy59",
+    "60": "dummy60",
+    "61": "dummy61",
+    "62": "dummy62",
+    "63": "dummy63",
+    "64": "dummy64",
+    "65": "dummy65",
+    "66": "dummy66",
+    "67": "dummy67",
+    "68": "dummy68",
+    "69": "dummy69",
+    "70": "dummy70",
+    "71": "dummy71",
+    "72": "dummy72",
+    "73": "dummy73",
+    "74": "dummy74",
+    "75": "dummy75",
+    "76": "dummy76",
+    "77": "dummy77",
+    "78": "dummy78",
+    "79": "dummy79",
+    "80": "dummy80",
+    "81": "dummy81",
+    "82": "dummy82",
+    "83": "dummy83",
+    "84": "dummy84",
+    "85": "dummy85",
+    "86": "dummy86",
+    "87": "dummy87",
+    "88": "dummy88",
+    "89": "dummy89",
+    "90": "dummy90",
+    "91": "dummy91",
+    "92": "dummy92",
+    "93": "dummy93",
+    "94": "dummy94",
+    "95": "dummy95",
+    "96": "dummy96",
+    "97": "dummy97",
+    "98": "dummy98",
+    "99": "dummy99",
+    "100": "dummy100",
+    "101": "dummy101",
+    "102": "dummy102",
+    "103": "dummy103",
+    "104": "dummy104",
+    "105": "dummy105",
+    "106": "dummy106",
+    "107": "dummy107",
+    "108": "dummy108",
+    "109": "dummy109",
+    "110": "dummy110",
+    "111": "dummy111",
+    "112": "dummy112",
+    "113": "dummy113",
+    "114": "dummy114",
+    "115": "dummy115",
+    "116": "dummy116",
+    "117": "dummy117",
+    "118": "dummy118",
+    "119": "dummy119",
+    "120": "dummy120",
+    "121": "dummy121",
+    "122": "dummy122",
+    "123": "dummy123",
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