End of training

README.md

@@ -2,8 +2,6 @@
 license: other
 base_model: nvidia/mit-b0
 tags:
-- vision
-- image-segmentation
 - generated_from_trainer
 model-index:
 - name: segformer-b0-finetuned
@@ -15,14 +13,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 # segformer-b0-finetuned
 
-This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
+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: 0.5754
-- Mean Iou: 0.2781
-- Mean Accuracy: 0.3329
-- Overall Accuracy: 0.8463
-- Per Category Iou: [0.0, 0.7432161129630078, 0.854265404236928, 0.4606401052721709, 0.6557337899613191, 0.4079867997829282, nan, 0.37471812939221005, 0.2905341043386837, 0.0, 0.7537587486511262, 0.0, 0.0, nan, 0.0, 0.019848656872972055, 0.0, 0.0, 0.7115931639469374, nan, 0.3661808713379434, 0.13378413732653244, 0.0, nan, 0.0, 0.23570903658727577, 0.0, 0.0, 0.8461792428096935, 0.7553019453875489, 0.9045825383881589, 0.0, 0.0, 0.10651182264386322, 0.0]
-- Per Category Accuracy: [0.0, 0.8511274737458464, 0.9523527728262475, 0.7305783824446481, 0.7179823443918317, 0.5112934364530293, nan, 0.4671955914617317, 0.39620749876026823, 0.0, 0.9325380267720194, 0.0, 0.0, nan, 0.0, 0.019920987025907694, 0.0, 0.0, 0.9114075726560573, nan, 0.4767221960460328, 0.14080931640440494, 0.0, nan, 0.0, 0.2902864462270403, 0.0, 0.0, 0.9417630123717813, 0.8946072183599384, 0.9626510283976625, 0.0, 0.0, 0.12104456389804058, 0.0]
+- Loss: 2.9804
+- Mean Iou: 0.0270
+- Mean Accuracy: 0.0832
+- Overall Accuracy: 0.5068
+- Per Category Iou: [0.8021452931267776, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.00757909387963703, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.000748031223377361, 0.0, 0.15502171352863242, nan, 0.0, nan, 0.056221004367996964, nan, 0.0, nan, 0.0, nan, 0.040391599317767694, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.07047672462142457, 0.0, nan, nan, 0.05062781753814023, nan, 0.0, 0.03166986564299424, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.08493484197676761, 0.0, nan, 0.04267983360952349, nan, 0.0003925481280361144, 0.0, nan, nan, 0.00012044806680852773, 0.0, 0.0, 0.002362410370862814, nan, nan, nan, nan, 0.0022562095679258846, 0.0, 0.0]
+- Per Category Accuracy: [0.8694985349631393, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.008096131396611705, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0010361203050798676, nan, 0.8231816690745898, nan, 0.0, nan, 0.11201351351351352, nan, 0.0, nan, nan, nan, 0.07139561448697561, nan, nan, nan, nan, nan, nan, nan, 0.10334561484308179, 0.0, nan, nan, 0.12090652522991582, nan, 0.0, 0.037541019470575365, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.8687054097111588, 0.0, nan, 0.05824436171194732, nan, 0.00040998308819761186, nan, nan, nan, 0.00012146732528949713, 0.0, 0.0, 0.0023685118842166654, nan, nan, nan, nan, 0.0027728020023065785, nan, 0.0]
 
 ## Model description
 
@@ -42,27 +40,25 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 2
-- eval_batch_size: 2
+- train_batch_size: 1
+- eval_batch_size: 1
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 3
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                                                                                       | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                                                                                                |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| 0.6702        | 1.0   | 400  | 0.7027          | 0.2195   | 0.2629        | 0.8084           | [0.0, 0.6792133271879363, 0.7849474176188894, 0.058328120930117175, 0.6300690246185523, 0.25673461142351706, nan, 0.3004378389548008, 0.0, 0.0, 0.6990982425959871, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.6732565754528083, nan, 0.2872208279956378, 0.00018886917594771138, 0.0, nan, 0.0, 0.04832964515878562, 0.0, 0.0, 0.8103176781510323, 0.6868793807107686, 0.8837386972387465, 0.0, 0.0, 0.005328297121957592, 0.0]                             | [0.0, 0.766565968404771, 0.9690642801889813, 0.05881921997258986, 0.6774284161746986, 0.2997799346472589, nan, 0.3604784648706302, 0.0, 0.0, 0.9238506174053699, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.879793151515724, nan, 0.3814629549547224, 0.0001889329509116015, 0.0, nan, 0.0, 0.049252727470549255, 0.0, 0.0, 0.92204060605898, 0.9165746690826654, 0.9399161153753854, 0.0, 0.0, 0.005432287151031732, 0.0]                                 |
-| 0.3787        | 2.0   | 800  | 0.6242          | 0.2529   | 0.3048        | 0.8336           | [0.0, 0.7200155470846057, 0.8500725277201905, 0.4283923004744409, 0.6393695507210657, 0.35502977816991826, nan, 0.35184539253673836, 0.007092160389449536, 0.0, 0.7043921336269122, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.6907551328541987, nan, 0.30912243068319983, 0.08139332433133045, 0.0, nan, 0.0, 0.188100947571913, 0.0, 0.0, 0.8385996121617959, 0.7447284921504436, 0.8944303097872178, 0.0, 0.0, 0.03573598106370054, 0.0]                  | [0.0, 0.8883665924421738, 0.9304929976181545, 0.6379432034836245, 0.684408431327974, 0.47546406343261166, nan, 0.46904807570859863, 0.007098812013540027, 0.0, 0.9393400531924911, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9030339520321862, nan, 0.38990531390988364, 0.08456558485802895, 0.0, nan, 0.0, 0.22015360876747014, 0.0, 0.0, 0.9441960488646456, 0.8731757915405423, 0.9605664489132197, 0.0, 0.0, 0.0402316629096584, 0.0]                |
-| 0.5272        | 3.0   | 1200 | 0.5910          | 0.2640   | 0.3116        | 0.8409           | [0.0, 0.7417372358583919, 0.8491040334276788, 0.5026923409983705, 0.6531274799797995, 0.39671746797276214, nan, 0.3489393985838212, 0.10917691003765771, 0.0, 0.7340253134142348, 0.0, 0.0, nan, 0.0, 0.0015906711475390872, 0.0, 0.0, 0.6888929372303201, nan, 0.3000933215998536, 0.09430463167198322, 0.0, nan, 0.0, 0.22079460109263602, 0.0, 0.0, 0.8375430585634192, 0.7432674869295846, 0.8979452744557967, 0.0, 0.0, 0.06465850622764936, 0.0] | [0.0, 0.8742236634719254, 0.9469015516965127, 0.6845976124235456, 0.7153136135405865, 0.4838119970613863, nan, 0.426013369696913, 0.11595265302602359, 0.0, 0.9307602006332041, 0.0, 0.0, nan, 0.0, 0.0015906711475390872, 0.0, 0.0, 0.9211760279107508, nan, 0.3640489760089845, 0.09822101537391639, 0.0, nan, 0.0, 0.27522724799952525, 0.0, 0.0, 0.9529837430876597, 0.8328291532204077, 0.9626684254773068, 0.0, 0.0, 0.07271163516559737, 0.0] |
-| 1.0028        | 4.0   | 1600 | 0.5819          | 0.2749   | 0.3265        | 0.8451           | [0.0, 0.7442319582171808, 0.8549546101758252, 0.4558465282708946, 0.6592549345415454, 0.40147520263994, nan, 0.3560786579426865, 0.2724675418610539, 0.0, 0.7615078761694535, 0.0, 0.0, nan, 0.0, 0.01480792989181263, 0.0, 0.0, 0.6971675525618446, nan, 0.3289306001269004, 0.1400376526683254, 0.0, nan, 0.0, 0.2330975509072671, 0.0, 0.0, 0.8412274001878343, 0.7610379911113287, 0.9042555512089849, 0.0, 0.0, 0.09514392306437187, 0.0]         | [0.0, 0.8831147123698461, 0.9454704608007805, 0.7031575260834061, 0.7194367374187804, 0.4940337653992012, nan, 0.4313144685568867, 0.3654837110023501, 0.0, 0.911745245214873, 0.0, 0.0, nan, 0.0, 0.01483662361250094, 0.0, 0.0, 0.9249937012782616, nan, 0.39416005628239303, 0.1487585698177601, 0.0, nan, 0.0, 0.28903522220353906, 0.0, 0.0, 0.9446123320713813, 0.8885032163816529, 0.9544933330809051, 0.0, 0.0, 0.10758314548292006, 0.0]    |
-| 1.3105        | 5.0   | 2000 | 0.5754          | 0.2781   | 0.3329        | 0.8463           | [0.0, 0.7432161129630078, 0.854265404236928, 0.4606401052721709, 0.6557337899613191, 0.4079867997829282, nan, 0.37471812939221005, 0.2905341043386837, 0.0, 0.7537587486511262, 0.0, 0.0, nan, 0.0, 0.019848656872972055, 0.0, 0.0, 0.7115931639469374, nan, 0.3661808713379434, 0.13378413732653244, 0.0, nan, 0.0, 0.23570903658727577, 0.0, 0.0, 0.8461792428096935, 0.7553019453875489, 0.9045825383881589, 0.0, 0.0, 0.10651182264386322, 0.0]    | [0.0, 0.8511274737458464, 0.9523527728262475, 0.7305783824446481, 0.7179823443918317, 0.5112934364530293, nan, 0.4671955914617317, 0.39620749876026823, 0.0, 0.9325380267720194, 0.0, 0.0, nan, 0.0, 0.019920987025907694, 0.0, 0.0, 0.9114075726560573, nan, 0.4767221960460328, 0.14080931640440494, 0.0, nan, 0.0, 0.2902864462270403, 0.0, 0.0, 0.9417630123717813, 0.8946072183599384, 0.9626510283976625, 0.0, 0.0, 0.12104456389804058, 0.0]  |
+| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 4.0562        | 1.0   | 80   | 3.4450          | 0.0153   | 0.0686        | 0.4812           | [0.7793520968900188, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.02432875250844616, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0032801739760782893, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.12812692281805033, nan, 0.0, 0.0, 0.05380656279838841, nan, 0.0, nan, nan, nan, 0.0003018650007218511, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.008481858011825258, 0.0003758691975192633, nan, 0.0, 0.010826130083604932, 0.0, 0.0, 0.005665119052689867, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.07572116691493873, 0.0009061488673139158, nan, 0.00028572287888357543, 0.0, 0.0005691934965994337, 0.0, nan, nan, 0.0, 0.005145141665218147, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0016498912362201822, 0.0, 0.0] | [0.8320357969624665, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.044499683472624736, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0033331798720132593, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.843680423236109, nan, 0.0, nan, 0.1561081081081081, nan, 0.0, nan, nan, nan, 0.00030909131632584497, nan, nan, nan, nan, nan, nan, nan, 0.016399105204289756, 0.0004157427937915743, nan, nan, 0.01858790326504793, nan, 0.0, 0.005819295558958652, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.6029646048347995, 0.0009201324990798675, nan, 0.00029229869850158456, nan, 0.0006662225183211193, nan, nan, nan, 0.0, 0.0084153067606755, 0.0, 0.0, nan, nan, nan, nan, 0.002196157338110078, nan, 0.0] |
+| 3.4584        | 2.0   | 160  | 3.1139          | 0.0241   | 0.0797        | 0.4821           | [0.7782469073708838, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.010029985991945368, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0017241143388842517, 0.0, 0.1557832833948993, nan, 0.0, nan, 0.07580358123695485, nan, 0.0, nan, 0.0, nan, 0.028521816696650947, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.021143006947175254, 0.0, nan, nan, 0.018894200915169017, nan, 0.0, 0.0064420428056791695, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.06671080655150016, 0.0, nan, 0.016629320979883684, nan, 0.0010633250993799996, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.000702695105998854, 0.0, 0.0]                                                         | [0.8244354154654462, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.01064577392395212, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.002417614045186358, nan, 0.8166523587177286, nan, 0.0, nan, 0.23312162162162162, nan, 0.0, nan, nan, nan, 0.050991108025175265, nan, nan, nan, nan, nan, nan, nan, 0.026531350746759656, 0.0, nan, nan, 0.03812466546640066, nan, 0.0, 0.0066506234959527455, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.9177460691279352, 0.0, nan, 0.02032245161687333, nan, 0.0011103708638685321, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0007974873015483523, nan, 0.0]                                                       |
+| 3.1748        | 3.0   | 240  | 2.9804          | 0.0270   | 0.0832        | 0.5068           | [0.8021452931267776, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.00757909387963703, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.000748031223377361, 0.0, 0.15502171352863242, nan, 0.0, nan, 0.056221004367996964, nan, 0.0, nan, 0.0, nan, 0.040391599317767694, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.07047672462142457, 0.0, nan, nan, 0.05062781753814023, nan, 0.0, 0.03166986564299424, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.08493484197676761, 0.0, nan, 0.04267983360952349, nan, 0.0003925481280361144, 0.0, nan, nan, 0.00012044806680852773, 0.0, 0.0, 0.002362410370862814, nan, nan, nan, nan, 0.0022562095679258846, 0.0, 0.0]                         | [0.8694985349631393, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.008096131396611705, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0010361203050798676, nan, 0.8231816690745898, nan, 0.0, nan, 0.11201351351351352, nan, 0.0, nan, nan, nan, 0.07139561448697561, nan, nan, nan, nan, nan, nan, nan, 0.10334561484308179, 0.0, nan, nan, 0.12090652522991582, nan, 0.0, 0.037541019470575365, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.8687054097111588, 0.0, nan, 0.05824436171194732, nan, 0.00040998308819761186, nan, nan, nan, 0.00012146732528949713, 0.0, 0.0, 0.0023685118842166654, nan, nan, nan, nan, 0.0027728020023065785, nan, 0.0]                  |
 
 
 ### Framework versions
 
-- Transformers 4.38.2
+- Transformers 4.39.3
 - Pytorch 2.2.1+cu121
 - Datasets 2.18.0
 - Tokenizers 0.15.2

config.json

@@ -28,80 +28,218 @@
     256
   ],
   "id2label": {
-    "0": "unlabeled",
-    "1": "flat-road",
-    "2": "flat-sidewalk",
-    "3": "flat-crosswalk",
-    "4": "flat-cyclinglane",
-    "5": "flat-parkingdriveway",
-    "6": "flat-railtrack",
-    "7": "flat-curb",
-    "8": "human-person",
-    "9": "human-rider",
-    "10": "vehicle-car",
-    "11": "vehicle-truck",
-    "12": "vehicle-bus",
-    "13": "vehicle-tramtrain",
-    "14": "vehicle-motorcycle",
-    "15": "vehicle-bicycle",
-    "16": "vehicle-caravan",
-    "17": "vehicle-cartrailer",
-    "18": "construction-building",
-    "19": "construction-door",
-    "20": "construction-wall",
-    "21": "construction-fenceguardrail",
-    "22": "construction-bridge",
-    "23": "construction-tunnel",
-    "24": "construction-stairs",
-    "25": "object-pole",
-    "26": "object-trafficsign",
-    "27": "object-trafficlight",
-    "28": "nature-vegetation",
-    "29": "nature-terrain",
-    "30": "sky",
-    "31": "void-ground",
-    "32": "void-dynamic",
-    "33": "void-static",
-    "34": "void-unclear"
+    "0": "background",
+    "1": "candy",
+    "10": "cake",
+    "100": "oyster mushroom",
+    "101": "white button mushroom",
+    "102": "salad",
+    "103": "other ingredients",
+    "11": "wine",
+    "12": "milkshake",
+    "13": "coffee",
+    "14": "juice",
+    "15": "milk",
+    "16": "tea",
+    "17": "almond",
+    "18": "red beans",
+    "19": "cashew",
+    "2": "egg tart",
+    "20": "dried cranberries",
+    "21": "soy",
+    "22": "walnut",
+    "23": "peanut",
+    "24": "egg",
+    "25": "apple",
+    "26": "date",
+    "27": "apricot",
+    "28": "avocado",
+    "29": "banana",
+    "3": "french fries",
+    "30": "strawberry",
+    "31": "cherry",
+    "32": "blueberry",
+    "33": "raspberry",
+    "34": "mango",
+    "35": "olives",
+    "36": "peach",
+    "37": "lemon",
+    "38": "pear",
+    "39": "fig",
+    "4": "chocolate",
+    "40": "pineapple",
+    "41": "grape",
+    "42": "kiwi",
+    "43": "melon",
+    "44": "orange",
+    "45": "watermelon",
+    "46": "steak",
+    "47": "pork",
+    "48": "chicken duck",
+    "49": "sausage",
+    "5": "biscuit",
+    "50": "fried meat",
+    "51": "lamb",
+    "52": "sauce",
+    "53": "crab",
+    "54": "fish",
+    "55": "shellfish",
+    "56": "shrimp",
+    "57": "soup",
+    "58": "bread",
+    "59": "corn",
+    "6": "popcorn",
+    "60": "hamburg",
+    "61": "pizza",
+    "62": " hanamaki baozi",
+    "63": "wonton dumplings",
+    "64": "pasta",
+    "65": "noodles",
+    "66": "rice",
+    "67": "pie",
+    "68": "tofu",
+    "69": "eggplant",
+    "7": "pudding",
+    "70": "potato",
+    "71": "garlic",
+    "72": "cauliflower",
+    "73": "tomato",
+    "74": "kelp",
+    "75": "seaweed",
+    "76": "spring onion",
+    "77": "rape",
+    "78": "ginger",
+    "79": "okra",
+    "8": "ice cream",
+    "80": "lettuce",
+    "81": "pumpkin",
+    "82": "cucumber",
+    "83": "white radish",
+    "84": "carrot",
+    "85": "asparagus",
+    "86": "bamboo shoots",
+    "87": "broccoli",
+    "88": "celery stick",
+    "89": "cilantro mint",
+    "9": "cheese butter",
+    "90": "snow peas",
+    "91": " cabbage",
+    "92": "bean sprouts",
+    "93": "onion",
+    "94": "pepper",
+    "95": "green beans",
+    "96": "French beans",
+    "97": "king oyster mushroom",
+    "98": "shiitake",
+    "99": "enoki mushroom"
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   "initializer_range": 0.02,
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-    "void-ground": 31,
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+    " hanamaki baozi": "62",
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+    "almond": "17",
+    "apple": "25",
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+    "cauliflower": "72",
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+    "cheese butter": "9",
+    "cherry": "31",
+    "chicken duck": "48",
+    "chocolate": "4",
+    "cilantro mint": "89",
+    "coffee": "13",
+    "corn": "59",
+    "crab": "53",
+    "cucumber": "82",
+    "date": "26",
+    "dried cranberries": "20",
+    "egg": "24",
+    "egg tart": "2",
+    "eggplant": "69",
+    "enoki mushroom": "99",
+    "fig": "39",
+    "fish": "54",
+    "french fries": "3",
+    "fried meat": "50",
+    "garlic": "71",
+    "ginger": "78",
+    "grape": "41",
+    "green beans": "95",
+    "hamburg": "60",
+    "ice cream": "8",
+    "juice": "14",
+    "kelp": "74",
+    "king oyster mushroom": "97",
+    "kiwi": "42",
+    "lamb": "51",
+    "lemon": "37",
+    "lettuce": "80",
+    "mango": "34",
+    "melon": "43",
+    "milk": "15",
+    "milkshake": "12",
+    "noodles": "65",
+    "okra": "79",
+    "olives": "35",
+    "onion": "93",
+    "orange": "44",
+    "other ingredients": "103",
+    "oyster mushroom": "100",
+    "pasta": "64",
+    "peach": "36",
+    "peanut": "23",
+    "pear": "38",
+    "pepper": "94",
+    "pie": "67",
+    "pineapple": "40",
+    "pizza": "61",
+    "popcorn": "6",
+    "pork": "47",
+    "potato": "70",
+    "pudding": "7",
+    "pumpkin": "81",
+    "rape": "77",
+    "raspberry": "33",
+    "red beans": "18",
+    "rice": "66",
+    "salad": "102",
+    "sauce": "52",
+    "sausage": "49",
+    "seaweed": "75",
+    "shellfish": "55",
+    "shiitake": "98",
+    "shrimp": "56",
+    "snow peas": "90",
+    "soup": "57",
+    "soy": "21",
+    "spring onion": "76",
+    "steak": "46",
+    "strawberry": "30",
+    "tea": "16",
+    "tofu": "68",
+    "tomato": "73",
+    "walnut": "22",
+    "watermelon": "45",
+    "white button mushroom": "101",
+    "white radish": "83",
+    "wine": "11",
+    "wonton dumplings": "63"
   },
   "layer_norm_eps": 1e-06,
   "mlp_ratios": [
@@ -140,5 +278,5 @@
     2
   ],
   "torch_dtype": "float32",
-  "transformers_version": "4.38.2"
+  "transformers_version": "4.39.3"
 }

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