Image-to-Text
PyTorch
Safetensors
PEFT
English
remote-sensing
satellite-imagery
earth-observation
change-detection
visual-grounding
image-captioning
visual-question-answering
optical-sar-fusion
sar
multimodal
lora
Instructions to use thundercode/SatQuery with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use thundercode/SatQuery with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
|
Download RELEASE_MANIFEST.md from thundercode/SatQuery: direct link, hf CLI and curl.
- Browser
- Download file 12.5 kB
-
https://huggingface.co/thundercode/SatQuery/resolve/6df4c6ebaa2cfee7087723ec06fe4dee190b5634/RELEASE_MANIFEST.md
- Command line
-
hf download hf://thundercode/SatQuery@6df4c6ebaa2cfee7087723ec06fe4dee190b5634/RELEASE_MANIFEST.md
-
curl -L -o RELEASE_MANIFEST.md https://huggingface.co/thundercode/SatQuery/resolve/6df4c6ebaa2cfee7087723ec06fe4dee190b5634/RELEASE_MANIFEST.md
12.5 kB
Release Manifest
Repository: Anish-lab-blip/SatQuery-AI (public)
Generated: 2026-09-25T22:47:35+00:00
Generator: release/tools/generate_release_manifest.py (computed from disk, never typed)
Files: 53 · Total size: 9,366,709 bytes (9147.2 KiB)
Every file in this release, with its size and sha256. Verify a checkout with:
# for each row: echo '<sha256> <path>' | sha256sum -c -
| # | Path | Bytes | sha256 |
|---|---|---|---|
| 1 | HF_RELEASE_VERIFICATION.md |
6,443 | 1d87487c2e9db5fb86781076c5061e2090077dbe90f8728f848eab0c615cf43e |
| 2 | MODEL_CARD.md |
57,166 | d1ef979aa5cacd2271ebcafef343bd193f8bb2c201db79b0beb305de1b042d42 |
| 3 | README.md |
88,433 | 87d0ad3fd4078613ab5ecad7e8949836a728371619095f16cb241ab9e222cbd6 |
| 4 | docs/API_CONTRACT.md |
48,709 | 8a5f4d8635ee0d2149db491e3283cab14a55cadeb637ad5fb4f734eff4654fc9 |
| 5 | docs/ARCHITECTURE.md |
7,672 | 60611ca515e98a96beeae7a39e150532b577561b144c964f4a9a9fd2d66e470d |
| 6 | docs/BENCHMARKS.md |
55,999 | 97cfe327793443f60c98e6948c1080a4fecab0cf721252f119deb20cc6736c09 |
| 7 | docs/CHANGELOG.md |
36,879 | c2bfa8609db63801af2c46c7b8ecffae26c1685f22bcbb5e8b458d541c8a8309 |
| 8 | docs/DATASETS.md |
67,723 | 1aa5dc9715be4c94eb357d1066c79a6cc5d4c59ae4d587f249e3f1e66c1f628f |
| 9 | docs/DATA_PIPELINE.md |
66,285 | 24a543fd4ac270d0d58976dff1ab247610c4be7698a15f9380dbdf27c0f92c8b |
| 10 | docs/DEPLOYMENT.md |
45,603 | ff2c9291dcc2eca7c0723eb3e6a8da3bd2da1f1c084a18b941414301a379bcdf |
| 11 | docs/DEVELOPMENT.md |
69,355 | 52731bd5b187b911ee09ba03b17937096c1c1562cd6e3592ccacefe7b4881bbb |
| 12 | docs/EVALUATION.md |
111,672 | f35020b7a051409f1f53b7a1464eaa87252dd1dee6ea5bc378492e396063b48a |
| 13 | docs/FRONTEND.md |
73,926 | bb2f8b2fb7388329c526f9e2e3fa23dff9df21f86ef2751867d045b50dc6e9d4 |
| 14 | docs/GEOSPATIAL.md |
92,888 | ff2753fa10b5db4971ae949c828b7ecb5909b8ad9ed6bed287864ab23d7f0d6c |
| 15 | docs/GLOSSARY.md |
40,998 | 81f1a86d721e6517893bb56e5408b4315dd38a6b214e0f79f840b5ea9f8fc754 |
| 16 | docs/LIMITATIONS.md |
40,805 | 1205aab110bc0f34140d314ea6c2e723f37c973313d86339f8f7275a2bbed170 |
| 17 | docs/MASTER_ARCHITECTURE_PLAN.md |
65,304 | 64169e36069c777ad8d28501d7e6228af30fff5bd69d3b55806c38b11dc9365b |
| 18 | docs/MODELS.md |
103,145 | 5a3b166035a2ee9ea53f5aa3a2f751045494562181bba8f8a85f7babba063d6f |
| 19 | docs/OPERATIONS.md |
62,268 | 53ab5d5cb1686ba0e43abdde163dc2912b0fb860dc6e8081d192a01d7f184016 |
| 20 | docs/PERFORMANCE.md |
52,946 | 5c079ac08b7dc765af82a3dbf95e067b8797c8f9a30d23df9977252087c7fdee |
| 21 | docs/REPRODUCIBILITY.md |
65,200 | 28caf713d81ca516d9a5537e80c55aed8188a35797cf8917823097c561c641a4 |
| 22 | docs/RESEARCH_NOTES.md |
41,248 | 321226af9aaa903135730ea7a8a4e18a8d1999bdf30ff67b19417e34ab453651 |
| 23 | docs/SECURITY.md |
114,342 | 7841f002f14a7b341dbebf10a46dfa7c6ff3e12918e29cb619e5411b88407e3f |
| 24 | docs/SERVING.md |
75,797 | cd590b26c3f085dae29fa4f91b5f9ce93337f0b8a5a31d4f2ccad895babe0896 |
| 25 | docs/TESTING.md |
96,897 | 18745a84e1d98fcfa5498c586c9af1cc9dc8688351ac37ca4f8c11a1be9550e5 |
| 26 | docs/TRAINING.md |
90,697 | 7575748b37006b6353f511059ddf7c29cd210d84b47ec702b10eefc7297453f1 |
| 27 | docs/architecture/01-system-overview.md |
8,376 | 8df172840fcb5782b6d34665c32b061e4e345ce0957aa5804721340725c89913 |
| 28 | docs/architecture/02-deployment-topology.md |
85,979 | 0c7945e9bc1a54ba1982daae20dea40c79bd24544e7fba0b3fa42da61f37fb15 |
| 29 | docs/architecture/03-request-lifecycle.md |
130,175 | cec5e819e40686158cbf86e864a63022a029fc5497c04afac5e2648c678e0815 |
| 30 | docs/architecture/04-router.md |
262,652 | 48fda335045b4396b97002fca2e3e46af9206cf70276fcb31fa9967c14363db3 |
| 31 | docs/architecture/05-specialists.md |
252,171 | 2966131b2adcbe7e07e0126508dff8d5c4eaa898c750b64e30fd44effe33b1b4 |
| 32 | docs/architecture/06-evidence-and-confidence.md |
108,044 | 8ea1fe799d9af0ef7abb1face1048633834e28903c0bb21e9788236b440a9daa |
| 33 | docs/architecture/07-configuration-freeze.md |
106,023 | d46b779fdebdbe73036eff8092b1336fd2f2974d47bd1fa3e14b11bbe5a231ff |
| 34 | docs/architecture/08-api-contract.md |
127,341 | 357bfa8c518d2feead36252d8e9d46d183460497e87d68fe175cda2230ba310f |
| 35 | docs/architecture/09-frontend.md |
78,287 | 8d38bd2f2208271c533695feb22e67ea00bd529ede9cf2b5eb928a52d9bb5760 |
| 36 | docs/architecture/10-observability-and-ops.md |
104,695 | 4e660a6358d7a317ed2d49f3370919bec6cd233305f2795254bc3f4944a2438a |
| 37 | models/checksums.sha256 |
642 | f177e6fbc807868141378a75f36ca07cb01a8f859d9f80f1935fbf396ddf5380 |
| 38 | models/manifest.json |
4,458 | ae1f55cf906095e089c769efb774a8461dc20b7076752e7fbae1091255f38eb0 |
| 39 | screenshots/analyze-caption.png |
890,381 | 428abb8edff78812f2e1cfb23965f00572e89d741f6fd56bf6c5f3d4d500293f |
| 40 | screenshots/analyze-change-vqa.png |
694,618 | 0ddb4e6043443119db98ad4a70c637aacb27ec2404959f111bbe58c80c009e47 |
| 41 | screenshots/analyze-change.png |
445,200 | a833e4e975c70659769f499525b6182a0bfb17f1fad3d213e9f303954a9ada87 |
| 42 | screenshots/analyze-grounding-buildings.png |
1,036,080 | 9714f51020b224271f22a3be44fedb94f7c3b532cca68ba32179debb7edcb604 |
| 43 | screenshots/analyze-grounding-new-airport.png |
1,042,892 | 5d5af7d23c9f8c1f3724cb7775f1873f199442d1afbfa3726a43495780052555 |
| 44 | screenshots/analyze-grounding.png |
1,035,183 | b3199b55ea819d4955fcc713311c8fcb475f069aa99966e8fd577f21a16d184b |
| 45 | screenshots/analyze-optical-sar.png |
131,330 | 6006c8b35217904bfe1042137c22de4d8765bc57b1efb418a131df9ce8477b94 |
| 46 | screenshots/analyze-vqa.png |
1,104,701 | 7ee1d95fa1fb29fce86cf4a3545a8fa8811dbe4c18af3acc7816838b1412ad62 |
| 47 | tools/build_archive.py |
8,351 | 73574bb0ec85d5f7505b51661021f844dfd43cd13fafc802f75aa3835dd6a1ae |
| 48 | tools/generate_model_manifest.py |
6,868 | f445a43c888a30fcd868d810f1cacf8e682e8ed331755c661de2c29fb8d59550 |
| 49 | tools/generate_release_manifest.py |
4,100 | 9facdcd4a3a5d23adbaccf34c4c7a89d661e1bed47e7da5dc6977181a8ac5884 |
| 50 | tools/hf_verify.py |
3,878 | e6e59312bb9d20e103d7bd57b1924cc6fae0a4d1a552a2dd2fdbcce80885e86e |
| 51 | tools/readme_metrics_report.txt |
4,027 | 25108a07f671b8a33e5c312a81ce9d18e7a9c06b4955fb137a0f62e20c5bb2dc |
| 52 | tools/verify_archive.py |
4,946 | babb44dbb60497172a70d0b3c4abc26520bd0e37ef06c215e3f016f0d25a1abf |
| 53 | tools/verify_readme_metrics.py |
6,911 | 0ce1273aa5f847a56a38a4ca6601f50648d19cc1637394509ce5a9f1309ea388 |
Machine-readable listing
1d87487c2e9db5fb86781076c5061e2090077dbe90f8728f848eab0c615cf43e HF_RELEASE_VERIFICATION.md
d1ef979aa5cacd2271ebcafef343bd193f8bb2c201db79b0beb305de1b042d42 MODEL_CARD.md
87d0ad3fd4078613ab5ecad7e8949836a728371619095f16cb241ab9e222cbd6 README.md
8a5f4d8635ee0d2149db491e3283cab14a55cadeb637ad5fb4f734eff4654fc9 docs/API_CONTRACT.md
60611ca515e98a96beeae7a39e150532b577561b144c964f4a9a9fd2d66e470d docs/ARCHITECTURE.md
97cfe327793443f60c98e6948c1080a4fecab0cf721252f119deb20cc6736c09 docs/BENCHMARKS.md
c2bfa8609db63801af2c46c7b8ecffae26c1685f22bcbb5e8b458d541c8a8309 docs/CHANGELOG.md
1aa5dc9715be4c94eb357d1066c79a6cc5d4c59ae4d587f249e3f1e66c1f628f docs/DATASETS.md
24a543fd4ac270d0d58976dff1ab247610c4be7698a15f9380dbdf27c0f92c8b docs/DATA_PIPELINE.md
ff2c9291dcc2eca7c0723eb3e6a8da3bd2da1f1c084a18b941414301a379bcdf docs/DEPLOYMENT.md
52731bd5b187b911ee09ba03b17937096c1c1562cd6e3592ccacefe7b4881bbb docs/DEVELOPMENT.md
f35020b7a051409f1f53b7a1464eaa87252dd1dee6ea5bc378492e396063b48a docs/EVALUATION.md
bb2f8b2fb7388329c526f9e2e3fa23dff9df21f86ef2751867d045b50dc6e9d4 docs/FRONTEND.md
ff2753fa10b5db4971ae949c828b7ecb5909b8ad9ed6bed287864ab23d7f0d6c docs/GEOSPATIAL.md
81f1a86d721e6517893bb56e5408b4315dd38a6b214e0f79f840b5ea9f8fc754 docs/GLOSSARY.md
1205aab110bc0f34140d314ea6c2e723f37c973313d86339f8f7275a2bbed170 docs/LIMITATIONS.md
64169e36069c777ad8d28501d7e6228af30fff5bd69d3b55806c38b11dc9365b docs/MASTER_ARCHITECTURE_PLAN.md
5a3b166035a2ee9ea53f5aa3a2f751045494562181bba8f8a85f7babba063d6f docs/MODELS.md
53ab5d5cb1686ba0e43abdde163dc2912b0fb860dc6e8081d192a01d7f184016 docs/OPERATIONS.md
5c079ac08b7dc765af82a3dbf95e067b8797c8f9a30d23df9977252087c7fdee docs/PERFORMANCE.md
28caf713d81ca516d9a5537e80c55aed8188a35797cf8917823097c561c641a4 docs/REPRODUCIBILITY.md
321226af9aaa903135730ea7a8a4e18a8d1999bdf30ff67b19417e34ab453651 docs/RESEARCH_NOTES.md
7841f002f14a7b341dbebf10a46dfa7c6ff3e12918e29cb619e5411b88407e3f docs/SECURITY.md
cd590b26c3f085dae29fa4f91b5f9ce93337f0b8a5a31d4f2ccad895babe0896 docs/SERVING.md
18745a84e1d98fcfa5498c586c9af1cc9dc8688351ac37ca4f8c11a1be9550e5 docs/TESTING.md
7575748b37006b6353f511059ddf7c29cd210d84b47ec702b10eefc7297453f1 docs/TRAINING.md
8df172840fcb5782b6d34665c32b061e4e345ce0957aa5804721340725c89913 docs/architecture/01-system-overview.md
0c7945e9bc1a54ba1982daae20dea40c79bd24544e7fba0b3fa42da61f37fb15 docs/architecture/02-deployment-topology.md
cec5e819e40686158cbf86e864a63022a029fc5497c04afac5e2648c678e0815 docs/architecture/03-request-lifecycle.md
48fda335045b4396b97002fca2e3e46af9206cf70276fcb31fa9967c14363db3 docs/architecture/04-router.md
2966131b2adcbe7e07e0126508dff8d5c4eaa898c750b64e30fd44effe33b1b4 docs/architecture/05-specialists.md
8ea1fe799d9af0ef7abb1face1048633834e28903c0bb21e9788236b440a9daa docs/architecture/06-evidence-and-confidence.md
d46b779fdebdbe73036eff8092b1336fd2f2974d47bd1fa3e14b11bbe5a231ff docs/architecture/07-configuration-freeze.md
357bfa8c518d2feead36252d8e9d46d183460497e87d68fe175cda2230ba310f docs/architecture/08-api-contract.md
8d38bd2f2208271c533695feb22e67ea00bd529ede9cf2b5eb928a52d9bb5760 docs/architecture/09-frontend.md
4e660a6358d7a317ed2d49f3370919bec6cd233305f2795254bc3f4944a2438a docs/architecture/10-observability-and-ops.md
f177e6fbc807868141378a75f36ca07cb01a8f859d9f80f1935fbf396ddf5380 models/checksums.sha256
ae1f55cf906095e089c769efb774a8461dc20b7076752e7fbae1091255f38eb0 models/manifest.json
428abb8edff78812f2e1cfb23965f00572e89d741f6fd56bf6c5f3d4d500293f screenshots/analyze-caption.png
0ddb4e6043443119db98ad4a70c637aacb27ec2404959f111bbe58c80c009e47 screenshots/analyze-change-vqa.png
a833e4e975c70659769f499525b6182a0bfb17f1fad3d213e9f303954a9ada87 screenshots/analyze-change.png
9714f51020b224271f22a3be44fedb94f7c3b532cca68ba32179debb7edcb604 screenshots/analyze-grounding-buildings.png
5d5af7d23c9f8c1f3724cb7775f1873f199442d1afbfa3726a43495780052555 screenshots/analyze-grounding-new-airport.png
b3199b55ea819d4955fcc713311c8fcb475f069aa99966e8fd577f21a16d184b screenshots/analyze-grounding.png
6006c8b35217904bfe1042137c22de4d8765bc57b1efb418a131df9ce8477b94 screenshots/analyze-optical-sar.png
7ee1d95fa1fb29fce86cf4a3545a8fa8811dbe4c18af3acc7816838b1412ad62 screenshots/analyze-vqa.png
73574bb0ec85d5f7505b51661021f844dfd43cd13fafc802f75aa3835dd6a1ae tools/build_archive.py
f445a43c888a30fcd868d810f1cacf8e682e8ed331755c661de2c29fb8d59550 tools/generate_model_manifest.py
9facdcd4a3a5d23adbaccf34c4c7a89d661e1bed47e7da5dc6977181a8ac5884 tools/generate_release_manifest.py
e6e59312bb9d20e103d7bd57b1924cc6fae0a4d1a552a2dd2fdbcce80885e86e tools/hf_verify.py
25108a07f671b8a33e5c312a81ce9d18e7a9c06b4955fb137a0f62e20c5bb2dc tools/readme_metrics_report.txt
babb44dbb60497172a70d0b3c4abc26520bd0e37ef06c215e3f016f0d25a1abf tools/verify_archive.py
0ce1273aa5f847a56a38a4ca6601f50648d19cc1637394509ce5a9f1309ea388 tools/verify_readme_metrics.py
Contents summary
| Area | What it is |
|---|---|
README.md |
the repository front page |
MODEL_CARD.md |
model card for the six trained artifacts |
docs/ |
the research + engineering documentation set |
docs/architecture/ |
the deep architecture reference (multi-part) |
models/manifest.json |
generated manifest of the six trained artifacts |
models/checksums.sha256 |
generated checksums for the six trained artifacts |
screenshots/ |
real live-run captures |
What this release deliberately does NOT contain
- Backbone weights. They are fetched from the Hugging Face Hub, pinned by revision.
- Secrets. No tokens, keys, or environment files.
- The private deployment repositories. Their sources are not published here.
- Datasets. Acquisition procedures are documented; the data is not redistributed.
- A licence file. None has been selected yet — this is an OPEN item.