End of training

README.md

@@ -1,199 +1,360 @@
 ---
-library_name: transformers
-tags: []
+license: other
+base_model: nvidia/segformer-b3-finetuned-ade-512-512
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b3-finetuned-UAVid
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
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-
-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
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-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
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-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
-
-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
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-
-#### Hardware
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-
-#### Software
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-
-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## Model Card Authors [optional]
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-## Model Card Contact
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+<!-- 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-b3-finetuned-UAVid
+
+This model is a fine-tuned version of [nvidia/segformer-b3-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b3-finetuned-ade-512-512) on the None dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.2144
+- eval_mean_iou: 0.6573
+- eval_mean_accuracy: 0.7194
+- eval_overall_accuracy: 0.9282
+- eval_accuracy_wall: nan
+- eval_accuracy_building: 0.9548
+- eval_accuracy_sky: nan
+- eval_accuracy_floor: nan
+- eval_accuracy_tree: 0.9445
+- eval_accuracy_ceiling: nan
+- eval_accuracy_road: 0.8933
+- eval_accuracy_bed : nan
+- eval_accuracy_windowpane: nan
+- eval_accuracy_grass: nan
+- eval_accuracy_cabinet: nan
+- eval_accuracy_sidewalk: nan
+- eval_accuracy_person: 0.0612
+- eval_accuracy_earth: nan
+- eval_accuracy_door: nan
+- eval_accuracy_table: nan
+- eval_accuracy_mountain: nan
+- eval_accuracy_plant: nan
+- eval_accuracy_curtain: nan
+- eval_accuracy_chair: nan
+- eval_accuracy_car: 0.7429
+- eval_accuracy_water: nan
+- eval_accuracy_painting: nan
+- eval_accuracy_sofa: nan
+- eval_accuracy_shelf: nan
+- eval_accuracy_house: nan
+- eval_accuracy_sea: nan
+- eval_accuracy_mirror: nan
+- eval_accuracy_rug: nan
+- eval_accuracy_field: nan
+- eval_accuracy_armchair: nan
+- eval_accuracy_seat: nan
+- eval_accuracy_fence: nan
+- eval_accuracy_desk: nan
+- eval_accuracy_rock: nan
+- eval_accuracy_wardrobe: nan
+- eval_accuracy_lamp: nan
+- eval_accuracy_bathtub: nan
+- eval_accuracy_railing: nan
+- eval_accuracy_cushion: nan
+- eval_accuracy_base: nan
+- eval_accuracy_box: nan
+- eval_accuracy_column: nan
+- eval_accuracy_signboard: nan
+- eval_accuracy_chest of drawers: nan
+- eval_accuracy_counter: nan
+- eval_accuracy_sand: nan
+- eval_accuracy_sink: nan
+- eval_accuracy_skyscraper: nan
+- eval_accuracy_fireplace: nan
+- eval_accuracy_refrigerator: nan
+- eval_accuracy_grandstand: nan
+- eval_accuracy_path: nan
+- eval_accuracy_stairs: nan
+- eval_accuracy_runway: nan
+- eval_accuracy_case: nan
+- eval_accuracy_pool table: nan
+- eval_accuracy_pillow: nan
+- eval_accuracy_screen door: nan
+- eval_accuracy_stairway: nan
+- eval_accuracy_river: nan
+- eval_accuracy_bridge: nan
+- eval_accuracy_bookcase: nan
+- eval_accuracy_blind: nan
+- eval_accuracy_coffee table: nan
+- eval_accuracy_toilet: nan
+- eval_accuracy_flower: nan
+- eval_accuracy_book: nan
+- eval_accuracy_hill: nan
+- eval_accuracy_bench: nan
+- eval_accuracy_countertop: nan
+- eval_accuracy_stove: nan
+- eval_accuracy_palm: nan
+- eval_accuracy_kitchen island: nan
+- eval_accuracy_computer: nan
+- eval_accuracy_swivel chair: nan
+- eval_accuracy_boat: nan
+- eval_accuracy_bar: nan
+- eval_accuracy_arcade machine: nan
+- eval_accuracy_hovel: nan
+- eval_accuracy_bus: nan
+- eval_accuracy_towel: nan
+- eval_accuracy_light: nan
+- eval_accuracy_truck: nan
+- eval_accuracy_tower: nan
+- eval_accuracy_chandelier: nan
+- eval_accuracy_awning: nan
+- eval_accuracy_streetlight: nan
+- eval_accuracy_booth: nan
+- eval_accuracy_television receiver: nan
+- eval_accuracy_airplane: nan
+- eval_accuracy_dirt track: nan
+- eval_accuracy_apparel: nan
+- eval_accuracy_pole: nan
+- eval_accuracy_land: nan
+- eval_accuracy_bannister: nan
+- eval_accuracy_escalator: nan
+- eval_accuracy_ottoman: nan
+- eval_accuracy_bottle: nan
+- eval_accuracy_buffet: nan
+- eval_accuracy_poster: nan
+- eval_accuracy_stage: nan
+- eval_accuracy_van: nan
+- eval_accuracy_ship: nan
+- eval_accuracy_fountain: nan
+- eval_accuracy_conveyer belt: nan
+- eval_accuracy_canopy: nan
+- eval_accuracy_washer: nan
+- eval_accuracy_plaything: nan
+- eval_accuracy_swimming pool: nan
+- eval_accuracy_stool: nan
+- eval_accuracy_barrel: nan
+- eval_accuracy_basket: nan
+- eval_accuracy_waterfall: nan
+- eval_accuracy_tent: nan
+- eval_accuracy_bag: nan
+- eval_accuracy_minibike: nan
+- eval_accuracy_cradle: nan
+- eval_accuracy_oven: nan
+- eval_accuracy_ball: nan
+- eval_accuracy_food: nan
+- eval_accuracy_step: nan
+- eval_accuracy_tank: nan
+- eval_accuracy_trade name: nan
+- eval_accuracy_microwave: nan
+- eval_accuracy_pot: nan
+- eval_accuracy_animal: nan
+- eval_accuracy_bicycle: nan
+- eval_accuracy_lake: nan
+- eval_accuracy_dishwasher: nan
+- eval_accuracy_screen: nan
+- eval_accuracy_blanket: nan
+- eval_accuracy_sculpture: nan
+- eval_accuracy_hood: nan
+- eval_accuracy_sconce: nan
+- eval_accuracy_vase: nan
+- eval_accuracy_traffic light: nan
+- eval_accuracy_tray: nan
+- eval_accuracy_ashcan: nan
+- eval_accuracy_fan: nan
+- eval_accuracy_pier: nan
+- eval_accuracy_crt screen: nan
+- eval_accuracy_plate: nan
+- eval_accuracy_monitor: nan
+- eval_accuracy_bulletin board: nan
+- eval_accuracy_shower: nan
+- eval_accuracy_radiator: nan
+- eval_accuracy_glass: nan
+- eval_accuracy_clock: nan
+- eval_accuracy_flag: nan
+- eval_iou_wall: nan
+- eval_iou_building: 0.9011
+- eval_iou_sky: nan
+- eval_iou_floor: nan
+- eval_iou_tree: 0.8984
+- eval_iou_ceiling: nan
+- eval_iou_road: 0.8076
+- eval_iou_bed : nan
+- eval_iou_windowpane: nan
+- eval_iou_grass: nan
+- eval_iou_cabinet: nan
+- eval_iou_sidewalk: nan
+- eval_iou_person: 0.0573
+- eval_iou_earth: nan
+- eval_iou_door: nan
+- eval_iou_table: nan
+- eval_iou_mountain: nan
+- eval_iou_plant: nan
+- eval_iou_curtain: nan
+- eval_iou_chair: nan
+- eval_iou_car: 0.6221
+- eval_iou_water: nan
+- eval_iou_painting: nan
+- eval_iou_sofa: nan
+- eval_iou_shelf: nan
+- eval_iou_house: nan
+- eval_iou_sea: nan
+- eval_iou_mirror: nan
+- eval_iou_rug: nan
+- eval_iou_field: nan
+- eval_iou_armchair: nan
+- eval_iou_seat: nan
+- eval_iou_fence: nan
+- eval_iou_desk: nan
+- eval_iou_rock: nan
+- eval_iou_wardrobe: nan
+- eval_iou_lamp: nan
+- eval_iou_bathtub: nan
+- eval_iou_railing: nan
+- eval_iou_cushion: nan
+- eval_iou_base: nan
+- eval_iou_box: nan
+- eval_iou_column: nan
+- eval_iou_signboard: nan
+- eval_iou_chest of drawers: nan
+- eval_iou_counter: nan
+- eval_iou_sand: nan
+- eval_iou_sink: nan
+- eval_iou_skyscraper: nan
+- eval_iou_fireplace: nan
+- eval_iou_refrigerator: nan
+- eval_iou_grandstand: nan
+- eval_iou_path: nan
+- eval_iou_stairs: nan
+- eval_iou_runway: nan
+- eval_iou_case: nan
+- eval_iou_pool table: nan
+- eval_iou_pillow: nan
+- eval_iou_screen door: nan
+- eval_iou_stairway: nan
+- eval_iou_river: nan
+- eval_iou_bridge: nan
+- eval_iou_bookcase: nan
+- eval_iou_blind: nan
+- eval_iou_coffee table: nan
+- eval_iou_toilet: nan
+- eval_iou_flower: nan
+- eval_iou_book: nan
+- eval_iou_hill: nan
+- eval_iou_bench: nan
+- eval_iou_countertop: nan
+- eval_iou_stove: nan
+- eval_iou_palm: nan
+- eval_iou_kitchen island: nan
+- eval_iou_computer: nan
+- eval_iou_swivel chair: nan
+- eval_iou_boat: nan
+- eval_iou_bar: nan
+- eval_iou_arcade machine: nan
+- eval_iou_hovel: nan
+- eval_iou_bus: nan
+- eval_iou_towel: nan
+- eval_iou_light: nan
+- eval_iou_truck: nan
+- eval_iou_tower: nan
+- eval_iou_chandelier: nan
+- eval_iou_awning: nan
+- eval_iou_streetlight: nan
+- eval_iou_booth: nan
+- eval_iou_television receiver: nan
+- eval_iou_airplane: nan
+- eval_iou_dirt track: nan
+- eval_iou_apparel: nan
+- eval_iou_pole: nan
+- eval_iou_land: nan
+- eval_iou_bannister: nan
+- eval_iou_escalator: nan
+- eval_iou_ottoman: nan
+- eval_iou_bottle: nan
+- eval_iou_buffet: nan
+- eval_iou_poster: nan
+- eval_iou_stage: nan
+- eval_iou_van: nan
+- eval_iou_ship: nan
+- eval_iou_fountain: nan
+- eval_iou_conveyer belt: nan
+- eval_iou_canopy: nan
+- eval_iou_washer: nan
+- eval_iou_plaything: nan
+- eval_iou_swimming pool: nan
+- eval_iou_stool: nan
+- eval_iou_barrel: nan
+- eval_iou_basket: nan
+- eval_iou_waterfall: nan
+- eval_iou_tent: nan
+- eval_iou_bag: nan
+- eval_iou_minibike: nan
+- eval_iou_cradle: nan
+- eval_iou_oven: nan
+- eval_iou_ball: nan
+- eval_iou_food: nan
+- eval_iou_step: nan
+- eval_iou_tank: nan
+- eval_iou_trade name: nan
+- eval_iou_microwave: nan
+- eval_iou_pot: nan
+- eval_iou_animal: nan
+- eval_iou_bicycle: nan
+- eval_iou_lake: nan
+- eval_iou_dishwasher: nan
+- eval_iou_screen: nan
+- eval_iou_blanket: nan
+- eval_iou_sculpture: nan
+- eval_iou_hood: nan
+- eval_iou_sconce: nan
+- eval_iou_vase: nan
+- eval_iou_traffic light: nan
+- eval_iou_tray: nan
+- eval_iou_ashcan: nan
+- eval_iou_fan: nan
+- eval_iou_pier: nan
+- eval_iou_crt screen: nan
+- eval_iou_plate: nan
+- eval_iou_monitor: nan
+- eval_iou_bulletin board: nan
+- eval_iou_shower: nan
+- eval_iou_radiator: nan
+- eval_iou_glass: nan
+- eval_iou_clock: nan
+- eval_iou_flag: nan
+- eval_runtime: 35.0873
+- eval_samples_per_second: 1.14
+- eval_steps_per_second: 0.57
+- epoch: 20.25
+- step: 1620
+
+## 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: 50
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,374 @@
+{
+  "_name_or_path": "nvidia/segformer-b3-finetuned-ade-512-512",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    4,
+    18,
+    3
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "wall",
+    "1": "building",
+    "2": "sky",
+    "3": "floor",
+    "4": "tree",
+    "5": "ceiling",
+    "6": "road",
+    "7": "bed ",
+    "8": "windowpane",
+    "9": "grass",
+    "10": "cabinet",
+    "11": "sidewalk",
+    "12": "person",
+    "13": "earth",
+    "14": "door",
+    "15": "table",
+    "16": "mountain",
+    "17": "plant",
+    "18": "curtain",
+    "19": "chair",
+    "20": "car",
+    "21": "water",
+    "22": "painting",
+    "23": "sofa",
+    "24": "shelf",
+    "25": "house",
+    "26": "sea",
+    "27": "mirror",
+    "28": "rug",
+    "29": "field",
+    "30": "armchair",
+    "31": "seat",
+    "32": "fence",
+    "33": "desk",
+    "34": "rock",
+    "35": "wardrobe",
+    "36": "lamp",
+    "37": "bathtub",
+    "38": "railing",
+    "39": "cushion",
+    "40": "base",
+    "41": "box",
+    "42": "column",
+    "43": "signboard",
+    "44": "chest of drawers",
+    "45": "counter",
+    "46": "sand",
+    "47": "sink",
+    "48": "skyscraper",
+    "49": "fireplace",
+    "50": "refrigerator",
+    "51": "grandstand",
+    "52": "path",
+    "53": "stairs",
+    "54": "runway",
+    "55": "case",
+    "56": "pool table",
+    "57": "pillow",
+    "58": "screen door",
+    "59": "stairway",
+    "60": "river",
+    "61": "bridge",
+    "62": "bookcase",
+    "63": "blind",
+    "64": "coffee table",
+    "65": "toilet",
+    "66": "flower",
+    "67": "book",
+    "68": "hill",
+    "69": "bench",
+    "70": "countertop",
+    "71": "stove",
+    "72": "palm",
+    "73": "kitchen island",
+    "74": "computer",
+    "75": "swivel chair",
+    "76": "boat",
+    "77": "bar",
+    "78": "arcade machine",
+    "79": "hovel",
+    "80": "bus",
+    "81": "towel",
+    "82": "light",
+    "83": "truck",
+    "84": "tower",
+    "85": "chandelier",
+    "86": "awning",
+    "87": "streetlight",
+    "88": "booth",
+    "89": "television receiver",
+    "90": "airplane",
+    "91": "dirt track",
+    "92": "apparel",
+    "93": "pole",
+    "94": "land",
+    "95": "bannister",
+    "96": "escalator",
+    "97": "ottoman",
+    "98": "bottle",
+    "99": "buffet",
+    "100": "poster",
+    "101": "stage",
+    "102": "van",
+    "103": "ship",
+    "104": "fountain",
+    "105": "conveyer belt",
+    "106": "canopy",
+    "107": "washer",
+    "108": "plaything",
+    "109": "swimming pool",
+    "110": "stool",
+    "111": "barrel",
+    "112": "basket",
+    "113": "waterfall",
+    "114": "tent",
+    "115": "bag",
+    "116": "minibike",
+    "117": "cradle",
+    "118": "oven",
+    "119": "ball",
+    "120": "food",
+    "121": "step",
+    "122": "tank",
+    "123": "trade name",
+    "124": "microwave",
+    "125": "pot",
+    "126": "animal",
+    "127": "bicycle",
+    "128": "lake",
+    "129": "dishwasher",
+    "130": "screen",
+    "131": "blanket",
+    "132": "sculpture",
+    "133": "hood",
+    "134": "sconce",
+    "135": "vase",
+    "136": "traffic light",
+    "137": "tray",
+    "138": "ashcan",
+    "139": "fan",
+    "140": "pier",
+    "141": "crt screen",
+    "142": "plate",
+    "143": "monitor",
+    "144": "bulletin board",
+    "145": "shower",
+    "146": "radiator",
+    "147": "glass",
+    "148": "clock",
+    "149": "flag"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "airplane": 90,
+    "animal": 126,
+    "apparel": 92,
+    "arcade machine": 78,
+    "armchair": 30,
+    "ashcan": 138,
+    "awning": 86,
+    "bag": 115,
+    "ball": 119,
+    "bannister": 95,
+    "bar": 77,
+    "barrel": 111,
+    "base": 40,
+    "basket": 112,
+    "bathtub": 37,
+    "bed ": 7,
+    "bench": 69,
+    "bicycle": 127,
+    "blanket": 131,
+    "blind": 63,
+    "boat": 76,
+    "book": 67,
+    "bookcase": 62,
+    "booth": 88,
+    "bottle": 98,
+    "box": 41,
+    "bridge": 61,
+    "buffet": 99,
+    "building": 1,
+    "bulletin board": 144,
+    "bus": 80,
+    "cabinet": 10,
+    "canopy": 106,
+    "car": 20,
+    "case": 55,
+    "ceiling": 5,
+    "chair": 19,
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